Method for operating a mover of a magnetic drive conveying system and related device

Abstract

The mover operation control method of the magnetic drive conveying system and the related equipment provided by the embodiments of the present application, the magnetic drive conveying system comprises a magnetic drive conveying track and a plurality of movers, the mover runs on the magnetic drive conveying track, and the method comprises the following steps: acquiring the current load mass of each mover and the current running distance between each adjacent two movers; based on the current load mass and the current running distance, the optimized running speed of each mover is calculated; based on the optimized running speed, the control change amount of each stator is calculated, and the predicted position of each mover at the predicted moment is calculated based on the control change amount; based on the current load mass and the optimized running speed, the running safety distance between each adjacent two movers is calculated; when the predicted position represents that the predicted running distance between each adjacent two movers exceeds the running safety distance, the stator is controlled based on the control change amount, so as to control the running of the mover, and the safety of the coordinated running of the plurality of movers is significantly improved.

Description

Technical Field

[0001] This application relates to the field of control technology, and in particular to a method and related equipment for controlling the movement of a mover in a magnetic drive conveyor system. Background Technology

[0002] Magnetic levitation transport technology has wide applications in industrial automation, such as assembling and packaging goods on logistics lines, and SMT (Surface Mount Technology) of precision electronic components. In these applications, movers typically run sequentially on magnetically driven transport tracks, performing processing operations on the workpieces on the movers during operation. To further improve the efficiency of the processing flow, appropriate operating parameters need to be assigned to each mover for mover operation control.

[0003] In existing technologies, before conveying multiple movers on a magnetic drive conveyor track, relevant kinematics formulas are typically used to pre-plan suitable operational parameters for each mover based on its initial velocity, initial position, target position, and its own operational parameters, in order to achieve a suitable processing flow for the workpiece on the mover. However, since the workpiece on the mover inevitably undergoes quality changes after processing, the mover cannot completely follow the original operational trajectory corresponding to the planned parameters. Moreover, since multiple movers typically operate on a magnetic drive conveyor track, the change in trajectory of the movers can easily cause collisions between them, posing a safety problem. Summary of the Invention

[0004] This application provides a method and related equipment for controlling the movement of multiple movers in a magnetic drive conveyor system, which can improve the safety of multi-motor operation in the magnetic drive conveyor system.

[0005] To achieve the above objectives, a first aspect of this application provides a method for controlling the movement of a mover in a magnetic drive conveyor system. The magnetic drive conveyor system includes a magnetic drive conveyor track and a plurality of movers. The magnetic drive conveyor track includes a plurality of stators. The movers run on the magnetic drive conveyor track. The method includes:

[0006] Obtain the current load mass of each of the movers, and obtain the current running distance between each pair of adjacent movers;

[0007] Based on the current load quality and the current running interval, the optimal running speed of each of the movers is calculated;

[0008] Based on the optimized operating speed, the control change amount of each stator is calculated, and based on the control change amount, the predicted position of each mover at the predicted time is calculated.

[0009] Based on the current load quality and the optimized operating speed, the safe operating distance between each pair of adjacent movers is calculated;

[0010] When the predicted position indicates that the predicted operating distance between any two adjacent movers exceeds the safe operating distance, the stator is controlled based on the control change amount to facilitate the operation control of the movers.

[0011] In some embodiments, calculating the optimized operating speed of each mover based on the current load quality and the current running interval includes:

[0012] Determine the load sensitivity weight for each of the movers based on the current load quality of each mover;

[0013] Based on the ratio of the running variable corresponding to each of the aforementioned actuators to the current running interval, and then multiplied by the corresponding load-sensitive weight, the running optimization function is obtained;

[0014] Based on the operating variables and the current load quality corresponding to each of the aforementioned actuators, a safety distance variable corresponding to each of the aforementioned actuators is obtained, and a safety distance constraint is obtained based on the numerical relationship between the current operating distance and the safety distance variable.

[0015] An operational optimization model is generated based on the operational optimization function and the safety distance constraint;

[0016] Solve the operation optimization model to obtain the solution corresponding to the operation variable of each of the movers as the optimized operation speed.

[0017] In some embodiments, determining the load sensitivity weight for each mover based on the current load quality of each mover includes:

[0018] Obtain the load sensitivity factor, and obtain the load sensitivity quality based on the product of the load sensitivity factor and the current load quality;

[0019] The load-sensitive weight is obtained based on the exponential processing of the load-sensitive quality.

[0020] In some embodiments, solving the operational optimization model to obtain the solution corresponding to the operational variable of each actuator as the optimized operational speed includes:

[0021] The safety distance constraint is relaxed to obtain the safety distance relaxation constraint;

[0022] The operational optimization model is transformed based on the aforementioned safety distance relaxation constraint to obtain a penalty-constrained operational model;

[0023] Based on the quadratic programming method, the penalty constraint running model is solved to obtain the solution corresponding to the running variable of each mover as the optimized running speed.

[0024] In some embodiments, calculating the control change amount for each stator based on the optimized operating speed includes:

[0025] From the multiple stators, determine the stator that is currently in the position of each mover as the corresponding control stator;

[0026] Obtain the control cost function corresponding to each of the control stators;

[0027] The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method. The solved control variable parameters are used as the control change amount of each control stator.

[0028] In some embodiments, obtaining the control cost function corresponding to each of the control stators includes:

[0029] The efficiency term weight of each control stator is obtained based on the ratio of the load sensitivity weight of the actuator corresponding to each control stator to the current operating distance.

[0030] The control cost term is obtained by multiplying the efficiency term weight and the square of the optimized operating parameter, plus the smoothing term weight and the square of the control change parameter.

[0031] Based on the control cost terms for all predicted time steps, the control cost function corresponding to the control stator is obtained.

[0032] In some embodiments, calculating the predicted position of each mover at the predicted time based on the control change includes:

[0033] Based on the multiple relationship of the control cycle, the distance control matrix and the change control matrix are generated;

[0034] The predicted position of each mover at the predicted time is obtained by multiplying the current position of each mover at the current time with the distance control matrix, and adding the product of the change control matrix and the control change amount.

[0035] In some embodiments, calculating the safe operating distance between each pair of adjacent movers based on the current load quality and the optimized operating speed includes:

[0036] Of the two adjacent moving parts, the latter moving part is designated as the braking moving part;

[0037] The reaction distance is obtained by multiplying the optimized operating speed of the brake actuator and the system response time.

[0038] The braking distance is obtained by dividing the square of the optimized operating speed of the brake actuator by the maximum acceleration of the brake actuator.

[0039] Based on the reaction distance, the braking distance, and the safety margin, the safe operating distance between the two adjacent movers is obtained.

[0040] In some embodiments, when the predicted position characterizes a predicted operating distance between every two adjacent movers that does not exceed a safe operating distance, the method further includes:

[0041] Increase the load sensitivity factor to obtain an updated load sensitivity factor, and update the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain the updated load sensitivity weight;

[0042] Based on the updated load-sensitive weights, the optimized operating speed of each of the movers and the control change of each of the stators are updated, and the updated operating safety distance is obtained based on the updated optimized operating speeds and the updated load-sensitive weights.

[0043] The updated predicted position of each mover at the predicted time is obtained based on the updated control change.

[0044] The above parameter update process is repeated until the updated predicted position indicates that the predicted operating distance between any two adjacent movers exceeds the safe operating distance. The stator is then controlled according to the updated control change to facilitate the operation control of the movers.

[0045] In some embodiments, controlling the stator based on the control change includes:

[0046] Proportional feedback is obtained by multiplying the position difference between the predicted position and the current position of each mover by the proportional gain factor.

[0047] Differential feedback is obtained by multiplying the differential of the position difference of each mover by the differential gain factor.

[0048] The phase offset is obtained based on the current load mass of the mover corresponding to each stator and the optimized operating speed;

[0049] Based on the sinusoidal processing of the phase offset, and then multiplied by it to obtain the compensation term amplitude, dynamic feedforward compensation is obtained.

[0050] Based on the accumulated values ​​of the proportional feedback, the differential feedback, and the dynamic feedforward compensation, the phase compensation current corresponding to each stator is obtained, and the corresponding stator is controlled based on the phase compensation current and the control change amount.

[0051] In some embodiments, obtaining the phase offset based on the current load mass of the mover corresponding to each stator and the optimized operating speed includes:

[0052] The rated thrust is obtained by multiplying the thrust coefficient by the rated current.

[0053] The command acceleration of each mover is obtained based on the difference between the optimized running speed of each mover and the current running speed at the current moment;

[0054] The thrust ratio is obtained by multiplying the current load mass of the mover corresponding to each stator and the commanded acceleration, and then dividing by the rated thrust.

[0055] Based on the arctangent processing of the thrust ratio, the phase offset corresponding to each stator is obtained.

[0056] To achieve the above objectives, a second aspect of this application provides a mover operation control device for a magnetic drive conveyor system. The magnetic drive conveyor system includes a magnetic drive conveyor track and a plurality of movers. The magnetic drive conveyor track includes a plurality of stators. The movers run on the magnetic drive conveyor track. The device includes:

[0057] The data acquisition module is used to acquire the current load mass of each of the movers and the current running distance between each pair of adjacent movers;

[0058] The speed optimization module is used to calculate the optimized operating speed of each of the movers based on the current load quality and the current running interval;

[0059] The position prediction module is used to calculate the control change of each stator based on the optimized running speed, and to calculate the predicted position of each mover at the prediction time based on the control change.

[0060] The safe distance calculation module is used to calculate the safe operating distance between each pair of adjacent actuators based on the current load quality and the optimized operating speed.

[0061] The operation control module is used to control the stator based on the control change amount when the predicted operating distance between any two adjacent movers, as indicated by the predicted position, exceeds the safe operating distance, so as to facilitate the operation control of the movers.

[0062] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the mover operation control method of the magnetic drive conveyor system as described in the first aspect.

[0063] To achieve the above objectives, a fourth aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the mover operation control method of the magnetic drive conveyor system described in the first aspect.

[0064] This application proposes a method and related equipment for controlling the movement of a magnetic drive conveyor system. The magnetic drive conveyor system includes a magnetic drive conveyor track and multiple movers. The magnetic drive conveyor track includes multiple stators. The movers run on the magnetic drive conveyor track. The method includes: First, obtaining the current load mass of each mover and the current running distance between each pair of adjacent movers; then, calculating the optimized running speed of each mover based on the current load mass and the current running distance; next, calculating the control change amount of each stator based on the optimized running speed, and calculating the predicted position of each mover at the predicted time based on the control change amount; second, calculating the safe running distance between each pair of adjacent movers based on the current load mass and the optimized running speed; finally, when the predicted position indicates that the predicted running distance between each pair of adjacent movers exceeds the safe running distance, controlling the stator based on the control change amount to facilitate the operation control of the movers. This application addresses the problem of track deviation and collisions caused by changes in mover load, which negatively impact processing efficiency. It obtains the current load mass of each mover and the current running distance between adjacent movers in real time, and dynamically calculates the optimal running speed of each mover and the dynamic safe running distance that must be maintained between them based on this real-time information. It also incorporates a mechanism for predicting the future position of the movers and a safety verification mechanism, ensuring that mover operation control is only executed when the predicted future mover distance meets the dynamically calculated safety distance requirement. This effectively overcomes the operational uncertainties caused by changes in workpiece quality during processing, proactively avoids collision risks that may result from mover track deviations, and significantly improves the safety, stability, and adaptive adjustment capability of multi-motor collaborative operation to dynamic working conditions. This ensures the continuity and reliability of the processing flow and provides strong support for further improving overall production efficiency and system robustness.

[0065] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the structure of a magnetic drive conveying system provided in one embodiment of this application.

[0067] Figure 2 This is a flowchart of a mover operation control method for a magnetic drive conveyor system provided in another embodiment of this application.

[0068] Figure 3 yes Figure 2 The flowchart for step 202.

[0069] Figure 4 yes Figure 3 The flowchart for step 301.

[0070] Figure 5 yes Figure 3 The flowchart for step 305.

[0071] Figure 6 yes Figure 2 The flowchart for step 203.

[0072] Figure 7 This is a schematic diagram of a control stator provided in another embodiment of this application.

[0073] Figure 8 yes Figure 6 The flowchart for step 602.

[0074] Figure 9 yes Figure 2 Another flowchart for step 203.

[0075] Figure 10 yes Figure 2 The flowchart for step 204.

[0076] Figure 11 This is a flowchart of parameter iterative update provided in another embodiment of this application.

[0077] Figure 12 yes Figure 2 The flowchart for step 205.

[0078] Figure 13 yes Figure 12 The flowchart for step 1203.

[0079] Figure 14This is a schematic diagram of the structure of the mover operation control device of a magnetic drive conveyor system provided in another embodiment of this application.

[0080] Figure 15 This is a schematic diagram of the hardware structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0081] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0082] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0083] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0084] Magnetic levitation transport technology has wide applications in industrial automation, such as assembling and packaging goods on logistics lines, and SMT (Surface Mount Technology) of precision electronic components. In these applications, movers typically run sequentially on magnetically driven transport tracks, performing processing operations on the workpieces on the movers during operation. To further improve the efficiency of the processing flow, appropriate operating parameters need to be assigned to each mover for mover operation control.

[0085] In existing technologies, before conveying multiple movers on a magnetic drive conveyor track, relevant kinematics formulas are typically used to pre-plan suitable operational parameters for each mover based on its initial velocity, initial position, target position, and its own operational parameters, in order to achieve a suitable processing flow for the workpiece on the mover. However, since the workpiece on the mover inevitably undergoes quality changes after processing, the mover cannot completely follow the original operational trajectory corresponding to the planned parameters. Moreover, since multiple movers typically operate on a magnetic drive conveyor track, the change in trajectory of the movers can easily cause collisions between them, posing a safety problem.

[0086] To improve the safety of multi-moving unit operation in magnetic drive conveyor systems, this application addresses the problem of moving unit load changes causing trajectory deviations, collisions, and consequently affecting processing efficiency. It acquires the current load mass of each moving unit and the current running distance between adjacent moving units in real time, and dynamically calculates the optimal running speed of each moving unit and the dynamic safe running distance they must maintain based on this real-time information. Furthermore, it incorporates a mechanism for predicting and verifying the future position of the moving units, ensuring that movement control is only executed when the predicted future moving unit distance meets the dynamically calculated safe distance requirement. This effectively overcomes the operational uncertainties caused by changes in workpiece quality during processing, proactively avoids collision risks due to moving unit trajectory deviations, and significantly improves the safety, stability, and adaptive adjustment capability of multi-moving unit collaborative operation to dynamic working conditions. This ensures the continuity and reliability of the processing flow and provides strong support for further improving overall production efficiency and system robustness.

[0087] To better illustrate the mover operation control method for the synchronous transition track provided in this application, this embodiment first describes a maglev transport track applying the mover operation control method. (Refer to...) Figure 1 The diagram shown is a structural schematic of a magnetic drive conveying system provided in an embodiment of this application. Figure 1 As shown, the physical environment in which the mover operation control method of this invention is applied is intuitively presented: that is, a scenario in which multiple movers operate collaboratively on the same magnetic drive transport track. The magnetic drive transport system includes a magnetic drive transport track and movers. The movers run on the magnetic drive transport track, which is a magnetic levitation transport track. The magnetic drive transport track mainly consists of multiple stators, in which electromagnetic coils are wound. By changing the electromagnetic parameters (including current magnitude, current direction, voltage intensity, voltage frequency, etc.) of the electromagnetic coils in each stator, the electromagnetic field generated by that stator can be changed. This field then senses the magnetic induction components (such as permanent magnets) carried by the movers running on the stators, applying a changing magnetic thrust to the movers, thus allowing the movers' operating data (such as acceleration, speed, etc.) to be changed in real time as needed.

[0088] Based on the above-described magnetic drive conveyor system, the mover operation control method of the magnetic drive conveyor system in the embodiments of this application will be described in detail below. (Refer to...) Figure 2 This is an optional flowchart of the mover operation control method for the magnetic drive conveyor system provided in the embodiments of this application. Figure 2 The method may include, but is not limited to, steps 201 to 205. It is also understood that this embodiment... Figure 2The order of steps 201 to 205 is not specifically limited; the order of steps can be adjusted or certain steps can be added or removed according to actual needs. The mover operation control method of the magnetic drive conveyor system provided in this application embodiment can be applied to intelligent terminals, servers, computers, etc., connected to the magnetic drive conveyor track.

[0089] Step 201: Obtain the current load mass of each mover and the current running distance between each pair of adjacent movers.

[0090] Step 201 will be described in detail below.

[0091] In some embodiments, when multiple movers operate together on a magnetic drive conveyor track, the magnetic drive conveyor system performs real-time sensing of the system status. Specifically, this includes accurately acquiring the current load mass carried by each mover operating on the magnetic drive conveyor track. The current load mass refers to the mass of the workpiece carried on the mover during processing, and is a key parameter affecting its subsequent kinematic and dynamic characteristics. It also includes measuring and acquiring the current running distance between every two adjacent movers in the running queue; this distance is an important basis for assessing and ensuring the safety of system operation; and includes the real-time position and real-time velocity of the movers. These real-time acquired dynamic parameters collectively form the basis for subsequent optimized control decisions.

[0092] The current running distance between two adjacent movers can be detected in real time using data obtained from the feedback ruler. i Accuracy ±0.1mm; the current load mass of each mover can be measured by an embedded piezoelectric thin-film sensor. i The real-time velocity and position of each mover can be fed back via a Hall array encoder. i With position x i Positioning accuracy is 0.01mm.

[0093] In some embodiments, the response delay of the system itself (including sensor sampling and calculation delays) also needs to be considered during which the distance the mover moves is s. rection =v*τ, where v is the average velocity of the mover during the response delay period, and τ is the experimentally calibrated system response time (i.e., the time from triggering the planning to the actual response of the mover motor, which is generally 6ms).

[0094] In addition, for each mover, its braking distance (i.e., the deceleration distance under the current load) is obtained by the dynamic formula shown in the following formula (1).

[0095]

[0096] Among them, ffriction f is the kinetic friction force generated when the small wheel moves on the guide rail. friction =μmg,F motor Electromagnetic thrust is the force output when the trolley is driven to move, i.e., F. motor =k I I coil -k d I coil 2 ;k I k is the linear thrust coefficient. d is the magnetic saturation nonlinear coefficient, which is usually determined by fitting the coil current-thrust experimental curve, μ is the friction coefficient, and g is the gravity coefficient.

[0097] In addition, the moving part distance error caused by the operating error of the magnetic drive conveyor system and sensor noise also needs to be considered, i.e., a safety margin of δ. margin =3σ, where σ is the standard distance difference of the system's overall error. The main function of the safety margin is to provide an additional safety buffer for the system to balance model errors (the theoretical calculation of the dynamic safety distance model may deviate from the actual value), sensor noise (random errors exist in the measurement data of sensors such as laser TOF and piezoelectric films), and environmental interference (such as the influence of external factors such as track vibration and temperature changes on the motion of the mover).

[0098] In addition, the calibration of electromagnetic parameters includes: electromagnetic thrust calibration, measuring the acceleration a under different currents when the load mass m = 0, and fitting F. motor =ma + μmg to get k I and k d Friction coefficient calibration: Adjust the track inclination angle θ until the mover slides down at a uniform speed, and measure μ = tanθ.

[0099] Step 202: Calculate the optimal running speed of each mover based on the current load quality and current running interval.

[0100] Step 202 will be described in detail below.

[0101] In some embodiments, after obtaining the system state of the magnetic drive conveyor system, the current load mass of each mover and the current operating distance between each pair of adjacent movers are used to calculate and determine the most suitable optimized operating speed for each mover under the current operating conditions through a preset optimization algorithm or model. This optimized operating speed aims to balance multiple objectives such as conveying efficiency, energy consumption, and operational stability, and is a dynamic programming of the ideal operating state of the movers, as described in detail below.

[0102] Reference Figure 3Based on the current load quality and the current running interval, the optimal running speed of each mover is calculated, including the following steps 301 to 305.

[0103] Step 301: Determine the load sensitivity weight for each mover based on the current load quality of each mover.

[0104] Step 301 will be described in detail below.

[0105] In some embodiments, to refine the subsequent speed optimization process, firstly for each actuator i, based on the current load mass m i To determine a corresponding load-sensitive weight w(m) i It is understandable that this load-sensitive weight is an adjustable parameter that reflects the degree of contribution or priority of movers under different load states to the overall optimization objective in subsequent optimization calculations, so that the optimization process can respond more precisely to the specific load conditions of each mover, as described below.

[0106] Reference Figure 4 The load sensitivity weight of each mover is determined based on the current load quality of each mover, including the following steps 401 to 402.

[0107] Step 401: Obtain the load sensitivity factor and get the load sensitivity quality based on the product of the load sensitivity factor and the current load quality.

[0108] Step 402: Based on the exponential processing of load-sensitive quality, obtain the load-sensitive weight.

[0109] Steps 401 to 402 are described in detail below.

[0110] In some embodiments, a corresponding load sensitivity factor α is set for each mover. This load sensitivity factor is a scalar that defines the sensitivity or adjustment direction of the current load mass in subsequent weight calculations. It can be customized as needed; it is also determined by energy consumption delay. Generally, the load sensitivity factor is high when the load mass is light and low when the load mass is heavy. Subsequently, the load sensitivity factor of each mover and the corresponding current load mass m are obtained. i Perform a product operation to obtain an intermediate quantity, namely the load-sensitive mass αm. i .

[0111] Then, the load-sensitive mass αm i Perform exponential processing to determine the load-sensitive mass αm. i The negative number is used as an exponential function to obtain the load-sensitive weight w(m) for each mover. i As shown in the following formula (2).

[0112]

[0113] By executing steps 401 and 402, the current load quality is initially linearly adjusted using the introduced load sensitivity factor to obtain the load-sensitive quality. Then, through a nonlinear mapping of the load-sensitive quality using exponential processing, the load-sensitive weight is finally obtained. This makes the calculation of the load-sensitive weight more flexible and adjustable. It can finely control the impact of the current load quality on the load-sensitive weight by adjusting the specific form of the load sensitivity factor and exponential processing according to different application scenarios and optimization objectives. This, in turn, affects the calculation of subsequent optimization running speed, so that the entire mover operation control strategy can better adapt to load changes and achieve specific performance optimization objectives.

[0114] Step 302: Based on the ratio of the running variable corresponding to each mover to the current running interval, multiply it by the corresponding load-sensitive weight to obtain the running optimization function.

[0115] Step 303: Based on the operating variables and current load quality corresponding to each mover, obtain the safety distance variable corresponding to each mover, and based on the numerical relationship between the current operating distance and the safety distance variable, obtain the safety distance constraint.

[0116] Step 304: Generate an operational optimization model based on the operational optimization function and safety distance constraints.

[0117] Step 305: Solve the running optimization model to obtain the solution corresponding to the running variables of each mover as the optimized running speed.

[0118] Steps 302 to 305 are described in detail below.

[0119] In some embodiments, the load-sensitive weight w(m) of each mover is obtained. i After that, further based on the running variable v corresponding to each mover... i and current running interval d i The ratio, multiplied by the corresponding load-sensitive weight w(m) i Then, the average process is performed to obtain the overall operation optimization function as shown in the following formula (3).

[0120]

[0121] Where n is the number of movers, the running optimization function (3) aims to quantify the passage speed per unit distance under different combinations of running variables (i.e. speed variables) under the current load and spacing conditions, and reflect the local passage efficiency.

[0122] To ensure operational safety, further adjustments are made based on the operating variables (i.e., velocity variable v) corresponding to each mover. i ) and its current load mass m i A dynamic safety distance variable s is calculated. safe (v i ,m i As shown in the following formula (4).

[0123]

[0124] Where τ is the system response time and 3σ is the safety margin. This safety distance variable represents the minimum theoretical distance required to avoid collision between this mover and its preceding or succeeding mover under specific operating variables and load conditions. Subsequently, the actual current operating distance d of each mover is compared... i The corresponding variable is the safety distance s. safe (v i ,m i The numerical relationship between these relationships (e.g., requiring the current running distance to be no less than the safety distance variable) is used to establish the safety distance constraint d. i ≥s safe (v i ,m i These safety distance constraints are boundary conditions that must be strictly followed during the optimization process to ensure basic safety during the cooperative operation of multiple movers.

[0125] Then, the running optimization function (3) is integrated with the safety distance constraint to formally generate a structured running optimization model as shown in the following formula (5).

[0126]

[0127] ST d i ≥s safe (v i ,m i (5)

[0128] Next, the generated operational optimization model (5) is solved. By employing a suitable mathematical optimization algorithm (such as quadratic programming, interior point method, etc., the specific algorithm selection can be based on the specific form of the model), the system can calculate one or a set of optimal solutions for the optimization model. That is, for each mover, the specific value of its corresponding operational variable in the optimal solution is determined as its final optimized operating speed, as described below.

[0129] Reference Figure 5 Solve the running optimization model to obtain the solution corresponding to the running variables of each mover as the optimized running speed, including the following steps 501 to 503.

[0130] Step 501: Relax the safety distance constraint to obtain the safety distance relaxation constraint.

[0131] Step 502: Transform the operation optimization model based on the safety distance relaxation constraint to obtain the penalty constraint operation model.

[0132] Step 503: Based on the quadratic programming method, solve the penalty constraint running model to obtain the solution corresponding to the running variables of each mover as the optimized running speed.

[0133] Steps 501 to 503 are described in detail below.

[0134] In some embodiments, in order to improve the robustness and feasibility of solving the optimization model, the method first relaxes the previously established safety distance constraints to obtain the safety distance relaxation constraints as shown in the following formula (6).

[0135] max(0,s safe -d i ) 2 (6)

[0136] Understandably, "relaxation" typically refers to transforming strict hard constraints (e.g., the spacing must be greater than or equal to a certain value) into soft constraints that can be violated to some extent, but with penalties for violations. Through this process, even under certain extreme or transient conditions where the original hard constraints are difficult to satisfy simultaneously, the optimization problem can still find a meaningful approximate solution instead of declaring it unsolvable.

[0137] Then, the safety distance relaxation constraint (6) is used to transform the operation optimization model (5), thereby integrating the safety distance relaxation constraint (6) as a penalty term into the operation optimization function (5), thus obtaining the penalty constraint operation model as shown in the following formula (7).

[0138]

[0139] Where λ is the penalty factor, which is on the order of 10^3. In this new penalty constraint running model (7), the optimization algorithm will automatically balance the relationship between satisfying the original optimization objective and minimizing the degree of violation of the relaxation constraint in the process of seeking the optimal solution.

[0140] Next, in this embodiment, the quadratic programming (QP) method is used to solve the penalty-constrained running model (7). Specifically, using motion variables, a state vector is defined. The objective function is rewritten as shown in the following formula (8).

[0141] J = v T Qv+c T v (8)

[0142] The relevant parameters are shown in the following formula (9).

[0143]

[0144] Then, the interior-point method is used to solve the problem, ensuring real-time performance: the Hessian matrix Q is pre-calculated using an FPGA, and the ARM core performs iterative solutions (with a convergence time limit, such as 10ms). Details are described below.

[0145] First, perform the relevant preprocessing: (1) Initialize the velocity vector v^0 (e.g., take the current velocity value); (2) Set the obstacle function parameters (e.g., logarithmic obstacle coefficient μ), initialize to a large value; (3) Define slack variables to handle inequality constraints. Then, the steps for pre-computing the Hessian matrix include (accelerated using FPGA): (1) Pre-compute the Hessian matrix H = -2Q (fixed value) of the objective function; (2) Store H and the gradient vector g = -c.

[0146] The iterative optimization steps then include (for ARM cores): (1) Newton step calculation: solving the linear equation system H·Δv=-g; (2) step size selection: determining the step size α by backtracking straight line search to ensure that the constraints are not violated; (3) variable update v k +1 =v k +α·Δv;(4) Obstacle parameter update: Gradually decrease μ to reduce the obstacle function weight.

[0147] After completing the interior point method solution, Hessian matrix pre-calculation, and iterative optimization, a final convergence check is performed to output the result.

[0148] The convergence judgment details are as follows: check if the gradient norm ||g|| < ε or if the maximum number of iterations has been reached (e.g., a 10ms time limit); if convergence has not been achieved, return to the above and continue iterative optimization.

[0149] In some embodiments, matrix operations (such as A·x, B·u) are mapped to hardware logic in the FPGA, with a single-step prediction time of <1µs, thereby improving the running speed.

[0150] By applying quadratic programming to solve the penalty-constrained operating model, the system can effectively calculate the optimal solution within this model framework. In this optimal solution, the specific value of the operating variable (i.e., the velocity variable) corresponding to each mover is determined as the final optimized operating velocity of that mover.

[0151] By executing steps 501 to 503, the safety distance constraint is first relaxed, transforming it into a safety distance relaxation constraint. This enhances the stability and adaptability of the model solution, avoiding potential solution failures caused by strict constraints. Based on this safety distance relaxation constraint, the original model is transformed into a penalty constraint running model. This allows the optimization process to satisfy the main objective while imposing controllable penalties for constraint violations. Furthermore, a quadratic programming method is explicitly used to efficiently solve this penalty constraint running model, obtaining the optimized running speed of each mover. This ensures the solvability of the optimization problem in complex and dynamic environments. Moreover, the mature quadratic programming method guarantees the efficiency and quality of the solution, enabling the calculated optimized running speed to balance safety, efficiency, and system stability in practical applications. This provides a solid algorithmic guarantee for achieving robust and high-performance mover operation control.

[0152] By executing steps 301 to 305, the importance of different load movers in the optimization objective is dynamically adjusted by introducing load-sensitive weights, and an operation optimization function guided by local traffic efficiency is constructed. At the same time, the safety distance constraint calculated based on real-time load and target speed is strictly followed. Finally, the optimized operating speed obtained by solving the operation optimization model can not only be personalized according to the real-time operating conditions of each mover (current load quality and current operating distance), ensuring the safety of multi-motor operation and the system's responsiveness to dynamic changes, but also maximize the overall operating efficiency and performance of the conveying system while ensuring safety.

[0153] Step 203: Calculate the control change of each stator based on the optimized operating speed, and calculate the predicted position of each mover at the predicted time based on the control change.

[0154] Step 203 will be described in detail below.

[0155] In some embodiments, based on the optimized operating speed of each mover, specific control commands for each stator on the magnetic drive transport track are further derived, and the future state of the movers is predicted. The stator refers to the fixed electromagnetic drive unit that constitutes the magnetic drive transport track.

[0156] At this point, based on the optimized running speed of each mover, combined with the current state of the mover (such as position and actual speed) and the dynamic model of the system, the control change that needs to be applied to the stator related to the current position of the mover is calculated. This control change can be reflected as the adjustment of driving current, voltage or electromagnetic force, which is a physical quantity that directly acts on the stator to drive the mover to move.

[0157] Subsequently, based on this control change and the dynamic model of the mover, the method can predictively calculate the predicted position of each mover at a future predicted time.

[0158] The following section first describes how to determine the control variation for each stator.

[0159] Reference Figure 6 The control change of each stator is calculated based on the optimized operating speed, including the following steps 601 to 603.

[0160] Step 601: From multiple stators, determine the stator that is currently in the position of each mover as the corresponding control stator.

[0161] Step 602: Obtain the control cost function corresponding to each control stator.

[0162] Steps 601 to 602 are described in detail below.

[0163] In some embodiments, in order to accurately convert the optimized operating speed of the mover into the corresponding control change of the stator, it is first necessary to identify the actuator that directly interacts with each mover electromagnetically at the current moment. (Refer to...) Figure 7 This is a schematic diagram of a control stator provided in an embodiment of this application. Figure 7 As shown, from the multiple stators constituting the magnetic drive conveyor track, based on the current real-time position information of each mover, one or more stators directly below or closest to it at the current moment, capable of applying effective driving force to it, are identified and determined, and these determined stators are used as the control stators corresponding to that mover, such as... Figure 7 The stator 2 corresponds to the actuator 2. This step ensures that subsequent control commands can be accurately applied to the correct physical execution unit.

[0164] After determining the control stator corresponding to each mover, this method needs to determine a corresponding control cost function for each control stator. This control cost function quantifies the cost or expected benefit of achieving a specific control objective (such as tracking the desired speed, minimizing energy consumption, and ensuring smooth motion) within a certain control cycle, as described below.

[0165] Reference Figure 8 To obtain the control cost function corresponding to each control stator, the steps 801 to 803 are as follows.

[0166] Step 801: Based on the ratio of the load sensitivity weight of the mover corresponding to each control stator to the current running distance, obtain the efficiency term weight of each control stator.

[0167] Step 802: Based on the product of the efficiency term weight and the square of the optimized operating parameter, plus the product of the smoothing term weight and the square of the control change parameter, the control cost term is obtained.

[0168] Step 803: Based on the control cost terms of all predicted time steps, obtain the control cost function corresponding to the control stator.

[0169] Steps 801 to 803 are described in detail below.

[0170] In some embodiments, in order to construct a control cost function that can effectively guide the behavior of the control stator, for each control stator, based on the ratio of the load sensitivity weight of the corresponding mover (reflecting the degree of influence of the mover load on the optimization objective) to the current running interval (reflecting the density of running and potential interactive effects), the efficiency term weight of each control stator is obtained as β = w(m i ) / d i This makes efficiency factors play a more important role in subsequent cost evaluation when the mover load sensitivity is high or the operating spacing is small.

[0171] Next, at the current time k, based on the efficiency term weight β and the square of the optimization operating parameters v i 2 The product of [k], plus the smoothing term weight γ and the square of the control variation parameter Δu i 2 The product of [k] yields the control cost term βv. i 2 [k]+γΔu i 2 [k]. Wherein, the control variation parameter Δu i [k]=u i [k]-u i [k-1] represents the control adjustment quantity applied to the stator that the underlying controller needs to solve, such as the change in current or force, i.e., the difference between control inputs (such as current or thrust commands) at adjacent moments. It reflects the magnitude of the change in control commands and is used to measure the severity of the control action.

[0172] In order to form a complete optimization objective that can guide the behavior of the control stator in a future time period, the control cost function corresponding to the control stator of the i-th mover is obtained based on the control cost terms of all predicted time steps, as shown in the following formula (10).

[0173]

[0174] By squaring the control input variation parameters and the optimization parameters and adding them to the cost function, the optimization process tends to choose strategies with smaller control input variations, thereby reducing frequent start-stops or sudden velocity changes of the mover and lowering mechanical shock and energy loss. The smoothing term weight γ is used to control smoothness; the larger the coefficient, the stricter the system's constraints on control input variations, and the smoother the response.

[0175] By executing steps 801 to 803, the upper-level planning information, such as the load sensitivity weight of the mover and the current operating distance, is effectively transmitted to the lower-level control objective through the calculation of the efficiency term weight. This ensures that efficiency considerations are closely integrated with real-time operating conditions. Furthermore, the control cost term is constructed in a way that balances the tracking of optimized operating parameters (efficiency objective) and the suppression of control parameter changes (smoothness objective), achieving a balance among multiple objectives. By accumulating the control cost term at all prediction time steps, the resulting control cost function endows the control decision with the ability to anticipate the future. This ensures that the control commands obtained based on this function not only pursue operating efficiency but also guarantee smoothness and stability of operation and can dynamically adapt to changes in system state. Thus, it provides a target guide for achieving high-performance and highly robust mover operation control.

[0176] Step 603: Substitute the optimized operating speed of each mover into the optimized operating parameters of the corresponding control cost function of the control stator, and solve the control variable parameters of each control cost function based on the quadratic programming method. Use the solved control variable parameters as the control change of each control stator.

[0177] Step 603 will be described in detail below.

[0178] In some embodiments, after obtaining the optimized operating speed of each mover and the corresponding control cost function (10) of the control stator, the optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method. The solved control variable parameters are used as the control change Δu of each control stator. i .

[0179] By executing steps 601 to 603, the specific control stators responsible for driving each mover are precisely identified, ensuring targeted control. Then, a control cost function is defined for each control stator to guide its behavior. Crucially, by using the optimized operating speed as the input or target of the control cost function, and again applying quadratic programming to solve for the control variable parameters, the control change is obtained. This allows the optimized operating speed planned at the upper level to effectively guide the specific behavior of the lower-level controller. Furthermore, the mature quadratic programming method ensures the efficiency and optimality of control command calculation, ultimately achieving precise, efficient, and overall optimization-compliant drive control for each mover.

[0180] Reference Figure 9 The predicted position of each mover at the predicted time is calculated based on the control change, including the following steps 901 to 902.

[0181] Step 901: Generate the distance control matrix and the change control matrix based on the multiple relationship of the control cycle.

[0182] Step 902: Based on the product of the current position of each mover at the current time and the distance control matrix, plus the product of the change control matrix and the control change amount, the predicted position of each mover at the predicted time is obtained.

[0183] Steps 901 to 902 are described in detail below.

[0184] In some embodiments, in order to predict the future position of the mover based on a discrete-time control system model, the method first needs to construct a state transition-related distance control matrix A and a change control matrix B based on a multiple of the control cycle in the magnetic drive control system, as shown in the following formula (11).

[0185]

[0186] The control cycle is the time interval between two control commands executed by the controller in the magnetic drive conveyor system; the distance control matrix typically describes how the current position state of the mover evolves to the state at the predicted time without external control input; and the change control matrix describes the effect of the control change applied at the current time on the state of the mover at the predicted time.

[0187] Next, using the generated distance control matrix and change control matrix, based on the discretized dynamic equations and the control change of the mover corresponding to the optimized operating parameters of the mover, the future position is specifically calculated. Specifically, for each mover, its current position at the current moment (and possible current velocity and other state variables, although the emphasis here is mainly on position) is multiplied with the distance control matrix to obtain a basic predicted state component, which reflects the future position evolution caused by the inherent dynamic characteristics of the system. Then, the control change to be applied to the stator corresponding to the mover, which was previously calculated, is multiplied with the change control matrix to obtain a predicted state increment caused by the control input. Finally, the two product results (i.e., the basic predicted component and the control increment) are vector-sumped to obtain the predicted position of each mover at a preset prediction time (such as six control cycles after the current moment, i.e., 6ms after), as shown in the following formula (12).

[0188] x i [k+1]=Ax i [k]+Bu i [k] (12)

[0189] By executing steps 901 and 902, a mathematical mapping relationship from the current state and control input to the future state is established by constructing distance control matrices and change control matrices related to the system control cycle and dynamic model. Using these matrices, the current position of the mover is combined with the control change to be applied, and the predicted position is accurately calculated in the form of standard discrete-time state-space equations. This allows the system to assess the possible future consequences of the control command before it is actually executed, especially the relative positional relationship between the movers, thus providing crucial forward-looking information for subsequent safety verification and control decisions.

[0190] Step 204: Based on the current load quality and optimized operating speed, calculate the safe operating distance between each pair of adjacent movers.

[0191] Step 204 will be described in detail below.

[0192] To ensure the safe operation of the movers, while calculating the predicted positions of the movers, the minimum safe distance, i.e., the operating safety distance, is precisely calculated in parallel based on the current load mass of each mover and its planned optimized operating speed, taking into account factors such as the mover's braking capacity and system response delay. This operating safety distance is a dynamically changing threshold that adaptively adjusts with changes in mover load and target speed to ensure sufficient safety redundancy under various operating conditions. Details are described below.

[0193] Reference Figure 10 Based on the current load quality and optimized operating speed, the safe operating distance between each pair of adjacent movers is calculated, including the following steps 1001 to 1004.

[0194] Step 1001: Take the latter of two adjacent movers as the braking mover.

[0195] Step 1002: Obtain the reaction distance based on the product of the optimized operating speed of the brake actuator and the system response time.

[0196] Step 1003: Based on the square of the optimized operating speed of the brake actuator, divide it by the maximum acceleration of the brake actuator to obtain the braking distance.

[0197] Step 1004: Based on the reaction distance, braking distance, and safety margin, obtain the safe operating distance between two adjacent movers.

[0198] Steps 1001 to 1004 are described in detail below.

[0199] In some embodiments, to explicitly calculate the safe operating distance between every two movers, for each pair of adjacent movers in the operating queue, the mover located at the rear of the queue is designated as the braking mover. Then, the safe distance variable s shown in formula (4) above is used. safe (v i ,m i Furthermore, based on the product of the optimized operating speed of the braking actuator and the system response time, the reaction distance v is obtained. i ·τ, this reaction distance represents the distance the brake actuator travels at its original optimized operating speed before it begins actual braking, due to the inherent response lag of the system. And, the braking distance v is obtained by dividing the square of the brake actuator's optimized operating speed by the maximum acceleration of the brake actuator under the current load mass. i 2 / 2a max (m i The braking distance represents the distance that a brake actuator must slide from the start of braking to a complete stop (or decelerate to the same speed as the vehicle in front) when braking at its maximum acceleration (maximum braking capacity). In addition, the current load mass of the brake actuator directly affects its maximum acceleration, so the braking distance is a quantity that changes dynamically with the load.

[0200] Finally, based on the sum of the reaction distance, braking distance, and safety margin, the safe operating distance s between two adjacent movers is obtained. safe .

[0201] By executing steps 1001 to 1004, the role of the braking actuator is clarified, and its reaction distance (considering system response time) and braking distance (considering the maximum acceleration under the influence of current load quality) at the optimized operating speed are calculated respectively. Then, a safety margin is added to this, and the final operating safety distance is a dynamic threshold that can truly reflect the safety requirements under the current working conditions. It can adaptively adjust according to the real-time current load quality of the actuator and the planned optimized operating speed for it, providing a reliable safety guarantee for subsequent operation control decisions. This effectively avoids collisions between actuators and improves the operational safety and reliability of the entire magnetic drive conveyor system.

[0202] Step 205: When the predicted position indicates that the predicted operating distance between any two adjacent movers exceeds the safe operating distance, the stator is controlled based on the control change to facilitate the operation control of the movers.

[0203] Step 205 will be described in detail below.

[0204] In some embodiments, after obtaining the predicted positions x of each mover... i After [k+1], the predicted position represents the predicted running distance x between every two adjacent movers at the predicted time. j [k+1]-x i [k+1] is greater than or equal to the safe operating distance s corresponding to these two movers. safe This indicates that, under the planned control change, the future operating state of the mover is safe. If this safety premise is met, the corresponding stator is actually driven by this control change to facilitate precise and safe operation control of the mover. If the predicted operating distance does not meet the safety distance requirements, the system takes other adjustment measures, as described below.

[0205] Reference Figure 11 When the predicted position characterizes the predicted operating distance between any two adjacent movers not exceeding the safe operating distance, the mover operation control method of the magnetic drive conveyor system further includes the following steps 1101 to 1104.

[0206] Step 1101: Increase the load sensitivity factor to obtain the updated load sensitivity factor, and update the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain the updated load sensitivity weight.

[0207] Step 1102: Update the optimized operating speed of each mover and the control change of each stator based on the updated load-sensitive weights, and obtain the updated operating safety distance based on the updated optimized operating speeds and updated load-sensitive weights.

[0208] Step 1103: Obtain the updated predicted position of each mover at the predicted time based on the updated control changes.

[0209] Step 1104: Repeat the above parameter update process until the updated predicted position characterizes the predicted operating distance between every two adjacent movers that exceeds the safe operating distance, and control the stator according to the updated control change to facilitate the operation control of the movers.

[0210] Steps 1101 to 1104 are described in detail below.

[0211] In some embodiments, when the previously calculated predicted positions indicate that the predicted running distance between any two adjacent movers does not exceed the previously calculated safe running distance, the system initiates an iterative adjustment mechanism to seek a safe control strategy. This includes increasing the load sensitivity factor α, which influences the load sensitivity in the optimization objective, to obtain an updated load sensitivity factor Δα. Subsequently, the load sensitivity weight w(m) of each mover is recalculated based on this updated load sensitivity factor Δα. i ), thus obtaining the updated load-sensitive weight Δw(m) i Increasing the load sensitivity factor usually means that in subsequent optimization calculations, the system will pay more attention to the heavier load mover, or tend to choose a speed scheme with lower energy consumption or smoother operation, which indirectly helps to alleviate potential congestion or collision risks.

[0212] Next, the load-sensitive weight Δw(m) is updated. i The optimized running speed is recalculated using the penalty constraint running model shown in formula (7) above, thus obtaining the updated optimized running speed for each mover. Simultaneously, based on this updated optimized running speed and the updated load-sensitive weight Δw(m)... i The system will recalculate the control changes for each stator. Furthermore, since the optimized operating speed has changed, the updated operating safety distance between each pair of adjacent movers is also recalculated based on the updated optimized operating speed and updated load sensitivity weights. After obtaining the updated control changes, the system needs to predict future mover positions again to assess the safety of the new control strategy.

[0213] Then, the above parameter update process is repeated, continuously updating a series of key parameters (load sensitivity weight, optimized operating speed, control change, operating safety distance, and predicted position) by adjusting the load sensitivity factor, until the predicted operating distance between each pair of adjacent movers, represented by the most recently calculated updated predicted position, finally exceeds the latest calculated updated operating safety distance. Once this safety condition is met, the iteration process terminates. At this point, based on the updated control change obtained in the last iteration and verified for safety, the corresponding stator is subjected to actual drive control to facilitate the operation control of the movers.

[0214] By executing steps 1101 to 1104, the load sensitivity factor is systematically increased, and a series of key parameters, including load sensitivity weight, optimized operating speed, control change amount, and operating safety distance, are updated accordingly. Then, the predicted position is recalculated and safety verification is performed. The control strategy can be actively and gradually adjusted until a solution that can meet the operating requirements and ensure future operating safety is found. This greatly enhances the robustness and safety of the system in complex and dynamic environments, enabling the magnetic drive conveyor system to self-correct and optimize when facing potential collision risks, and ultimately achieve safe, reliable and efficient mover operation control.

[0215] The following section will further describe how to suppress thrust fluctuations caused by load changes and improve motion stability while controlling the stator based on control variations.

[0216] Reference Figure 12 The stator is controlled based on the control change, including the following steps 1201 to 1205.

[0217] Step 1201: Obtain proportional feedback based on the product of the position difference between the predicted position and the current position of each mover and the proportional gain factor.

[0218] Step 1202: Based on the differential of the position difference of each mover, multiply it by the differential gain factor to obtain differential feedback.

[0219] Step 1203: Based on the current load mass and optimized operating speed of the mover corresponding to each stator, obtain the phase offset.

[0220] Steps 1201 to 1203 are described in detail below.

[0221] In some embodiments, in order to achieve precise closed-loop control of the stator, before controlling the mover, the product K of the position difference Δx between the predicted position of each mover at the predicted time and the current position at the current time and the proportional gain factor is also calculated. p Obtain proportional feedback K pΔx. The magnitude of this proportional feedback signal is proportional to the current position deviation, designed to respond quickly and reduce that deviation.

[0222] Furthermore, to enhance the dynamic performance and stability of the control system, the position difference Δx of each mover is differentiated over time to obtain the rate of change d(Δx) / dt of this position difference. Then, this differential result is compared with a preset differential gain factor K. d Multiplying them together, we obtain the differential feedback K. d d(Δx) / dt. This differential feedback signal is proportional to the rate of change of the position deviation, and can predict the trend of the deviation and intervene in advance, thereby suppressing overshoot, reducing oscillation, and improving the damping characteristics of the system.

[0223] Furthermore, to introduce feedforward control to compensate for known changes in system characteristics, a phase offset is calculated based on the current load mass of the mover corresponding to each stator and its planned optimized operating speed. This phase offset is typically related to the phase angle of the electromagnetic force required to drive the mover, especially in drive systems such as AC permanent magnet synchronous motors, where changes in load and speed significantly affect the optimal current phase required to generate the desired thrust. Therefore, this phase offset is an adjustment parameter pre-calculated based on the real-time operating conditions of the mover, as described below.

[0224] Reference Figure 13 Based on the current load mass and optimized operating speed of the mover corresponding to each stator, the phase offset is obtained, including the following steps 1301 to 1304.

[0225] Step 1301: Obtain the rated thrust based on the product of the thrust coefficient and the rated current.

[0226] Step 1302: Based on the difference between the optimized running speed of each mover and the current running speed at the current moment, obtain the command acceleration of each mover.

[0227] Step 1303: Based on the product of the current load mass and commanded acceleration of the mover corresponding to each stator, divide by the rated thrust to obtain the thrust ratio.

[0228] Step 1304: Based on the arctangent of the thrust ratio, obtain the phase offset corresponding to each stator.

[0229] Steps 1301 to 1304 are described in detail below.

[0230] In some embodiments, in order to establish the reference parameters required for calculating the phase offset, the method first bases the method on the stator (or motor) thrust coefficient k. I With rated current I nominalThe product of these two forces yields the maximum rated thrust F that the stator can generate under ideal conditions. nominal =k I I nominal .

[0231] Then, for each mover, the difference between its previously calculated optimized running speed and its current running speed at the current moment (i.e., the actual speed measured in real time by sensors) is obtained, and the instantaneous acceleration required for each mover to reach its target speed, i.e., the commanded acceleration a, is calculated. cmd This speed difference represents the amount of time (usually one control cycle) it takes for the mover to change its speed. Command acceleration is the acceleration the control system expects the mover to achieve in the next moment or within a short period.

[0232] Furthermore, for each stator and its corresponding mover, the current load mass is multiplied by the commanded acceleration. This product, according to Newton's second law (F=ma), represents the instantaneous thrust required to drive the mover to achieve the commanded acceleration. Then, this calculated required thrust is divided by the rated thrust to obtain a thrust ratio m·a. cmd / F nominal This thrust ratio reflects the percentage or proportion of the driving force required by the mover to the maximum capacity of its drive unit under the current operating conditions.

[0233] Ultimately based on the obtained thrust ratio m·a cmd / F nominal Perform arctangent processing (i.e., apply arctangent functions such as arctan or atan2) to obtain the phase offset φ(m,v) of the control stator corresponding to each mover.

[0234] By executing steps 1301 to 1304, the system's baseline rated thrust is determined using the thrust coefficient and rated current. Then, the command acceleration is derived based on the difference between the optimized operating speed and the current operating speed. Combined with the current load mass, the actual required thrust is calculated. This is then compared with the rated thrust to obtain the thrust ratio. By performing arctangent processing on the thrust ratio, the phase offset is accurately calculated. This dynamically reflects the current load condition and acceleration requirements of the mover, thereby guiding the underlying drive controller to adjust the distribution of excitation current and torque (thrust) current, or to adjust the current vector angle, so as to achieve the optimal thrust output efficiency and dynamic response characteristics at different operating points. This has significant beneficial effects on improving the energy utilization and control performance of the entire magnetic drive conveyor system.

[0235] Step 1204: Based on the sine processing of the phase offset, multiply it to obtain the compensation term amplitude value, and obtain the dynamic feedforward compensation.

[0236] Step 1205: Based on the accumulated values ​​of proportional feedback, differential feedback and dynamic feedforward compensation, obtain the phase compensation current corresponding to each stator, and control the corresponding stator based on the phase compensation current and control change.

[0237] Steps 1204 to 1205 are described in detail below.

[0238] In some embodiments, after obtaining the phase offset φ(m,v) corresponding to the control stator for each mover, the phase offset φ(m,v) is sinusoidally processed as sin(ωt+φ(m,v)), and then multiplied by the magnitude of the compensation term to obtain the dynamic feedforward compensation ηsin(ωt+φ(m,v)). This dynamic feedforward compensation aims to proactively adjust the phase of the drive current (or other relevant control parameters) according to the load of the mover and the desired speed, so as to generate the required driving force more efficiently and accurately, thereby responding in advance to foreseeable disturbances or dynamic changes.

[0239] Then, based on proportional feedback, differential feedback and dynamic feedforward compensation, algebraic accumulation is performed to obtain the comprehensive adjustment signal corresponding to each stator, that is, the phase compensation current as shown in the following formula (13).

[0240]

[0241] Finally, based on the phase compensation current obtained from this calculation and the previously calculated control change representing the macroscopic drive command, the corresponding stator is controlled together.

[0242] By executing steps 1201 to 1205, proportional feedback and differential feedback (forming a PD feedback controller) are used to ensure that the mover can accurately track the predicted position and quickly eliminate deviations. At the same time, dynamic feedforward compensation generated by the phase offset calculated based on the current load quality and optimized operating speed and subsequent sine processing actively adapts to the impact of changes in mover operating conditions on the drive characteristics. The phase compensation current formed by combining these feedback and feedforward signals with the basic control changes (as well as the control changes themselves) acts on the stator, realizing precise, stable and rapid closed-loop control of the mover's motion state. This effectively improves the positioning accuracy, anti-disturbance capability and overall dynamic performance of the magnetic drive conveyor system in actual operation.

[0243] In some embodiments, by implementing the mover operation control method of the magnetic drive conveyor system provided in this application, multiple tests have shown that, under the conditions of a mover load of 10kg and a spacing of 300mm, the passage efficiency is increased from 65% to 89%, the vibration amplitude is reduced from ±0.5mm to ±0.08mm, and the energy consumption is reduced by 32% under light load (2kg) conditions.

[0244] In some embodiments, compared with the prior art, this application achieves significant improvements in the core performance of magnetic drive conveyor systems, such as safety, efficiency, stability and energy consumption, through multimodal sensing fusion, dynamic safety modeling, distributed predictive control and electromagnetic cooperative compensation. The specific advantages include: (1) Significantly improved passage efficiency: Based on real-time load and spacing dynamic speed adjustment, the queue spacing utilization rate is optimized, and the transportation time is significantly shortened. Distributed predictive control reduces central scheduling delay and improves the overall throughput of the system; (2) Significantly improved motion stability: The electromagnetic phase compensation algorithm suppresses inertial force fluctuations, effectively reduces vibration during motion, and improves transportation stability; Load adaptive control dynamically adjusts the acceleration curve to eliminate light load overshoot and heavy load lag; (3) Outstanding energy consumption optimization effect: The dynamic PWM frequency modulation strategy matches the load demand and reduces energy waste under light load conditions; Electromagnetic thrust and load are adapted in real time to improve the overall energy efficiency ratio of the system; (4) Enhanced safety and robustness: The dynamic safety distance model calculates the safety threshold in real time, significantly reducing the risk of collision and the frequency of emergency braking; The predictive control mechanism identifies potential conflicts in advance and improves the stability of the system under dynamic disturbances.

[0245] This application proposes a method and related equipment for controlling the movement of a magnetic drive conveyor system. The magnetic drive conveyor system includes a magnetic drive conveyor track and multiple movers. The magnetic drive conveyor track includes multiple stators. The movers run on the magnetic drive conveyor track. The method includes: First, obtaining the current load mass of each mover and the current running distance between each pair of adjacent movers; then, obtaining a load sensitivity factor and obtaining a load sensitivity mass based on the product of the load sensitivity factor and the current load mass; obtaining a load sensitivity weight based on the exponential processing of the load sensitivity mass; obtaining an operation optimization function based on the ratio of the running variable corresponding to each mover to the current running distance, multiplied by the corresponding load sensitivity weight; obtaining a safety distance variable corresponding to each mover based on the running variable corresponding to each mover and the current load mass; obtaining a safety distance constraint based on the numerical relationship between the current running distance and the safety distance variable; generating an operation optimization model based on the operation optimization function and the safety distance constraint; solving the operation optimization model to obtain the solution corresponding to the running variable of each mover as the optimized running speed; next, from multiple stators... In this process, the stator at the current moment of each mover is identified as the corresponding control stator. The control cost function corresponding to each control stator is obtained. The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator. The control variable parameters of each control cost function are solved based on the quadratic programming method. The solved control variable parameters are used as the control change amount of each control stator. Based on the multiple relationship of the control period, a distance control matrix and a change control matrix are generated. The predicted position of each mover at the predicted moment is obtained by multiplying the current position of each mover at the current moment with the distance control matrix and adding the product of the change control matrix and the control change amount. Next, the mover that is later in two adjacent movers is taken as the braking mover. The reaction distance is obtained by multiplying the optimized operating speed of the braking mover with the system response time. The braking distance is obtained by dividing the square of the optimized operating speed of the braking mover by the maximum acceleration of the braking mover. The safe operating distance between two adjacent movers is obtained based on the reaction distance, braking distance, and safety margin.Finally, when the predicted position indicates that the predicted operating distance between any two adjacent movers exceeds the safe operating distance, proportional feedback is obtained based on the product of the position difference between the predicted and current positions of each mover and the proportional gain factor. Differential feedback is obtained by multiplying the differential of the position difference of each mover by the differential gain factor. Phase offset is obtained based on the current load mass and optimized operating speed of the mover corresponding to each stator. The compensation term amplitude is obtained by sine processing of the phase offset and multiplying it. Dynamic feedforward compensation is obtained. The phase compensation current corresponding to each stator is obtained by summing the proportional feedback, differential feedback, and dynamic feedforward compensation. The corresponding stator is then controlled based on the phase compensation current and control changes to facilitate mover operation control. When the predicted position... When the predicted operating distance between any two adjacent movers does not exceed the safe operating distance, the load sensitivity factor is increased to obtain an updated load sensitivity factor. Based on this updated load sensitivity factor, the load sensitivity weight of each mover is updated to obtain an updated load sensitivity weight. Based on the updated load sensitivity weight, the optimized operating speed of each mover and the control change of each stator are updated. Based on the updated optimized operating speed and updated load sensitivity weight, the updated safe operating distance is obtained. Based on the updated control change, the updated predicted position of each mover at the predicted time is obtained. This parameter update process is repeated until the updated predicted position indicates that the predicted operating distance between any two adjacent movers exceeds the safe operating distance. The stator is then controlled according to the updated control change to facilitate mover operation control.

[0246] This application addresses the problem of trajectory deviation and collisions caused by changes in mover load, which negatively impact processing efficiency. It obtains the current load mass of each mover and the current running distance between adjacent movers in real time, and dynamically calculates the optimal running speed of each mover and the dynamic safe running distance they must maintain based on this real-time information. It also incorporates a mechanism for predicting and verifying the future position of the movers, ensuring that movement control is only executed when the predicted future mover distance meets the dynamically calculated safe distance requirement. This effectively overcomes the operational uncertainties caused by changes in workpiece mass during processing, proactively avoids collision risks due to mover trajectory deviations, and significantly improves the safety, stability, and adaptive adjustment capability of multi-motor cooperative operation to dynamic working conditions. This ensures the continuity and reliability of the processing flow and provides... This provides strong support for further improving overall production efficiency and system robustness. Furthermore, by introducing load-sensitive weights to dynamically adjust the importance of different load movers in the optimization objective, and constructing an operation optimization function oriented towards local traffic efficiency, while strictly adhering to safety distance constraints calculated based on real-time load and target speed, the optimized operating speed obtained by solving the operation optimization model can not only be personalized according to the real-time operating conditions of each mover (current load quality and current operating distance), ensuring the safety of multi-motor operation and the system's responsiveness to dynamic changes, but also maximize the overall operating efficiency and performance of the conveying system while ensuring safety. Additionally, it precisely identifies which control stators should drive each mover, ensuring targeted control, and then defines a control cost function for each control stator to guide its behavior.Most importantly, by using the optimized operating speed as the input or objective of the control cost function, and again applying quadratic programming to solve for the control variable parameters of the control cost function, the control change is obtained. This allows the optimized operating speed of the upper-level planning to effectively guide the specific behavior of the lower-level controller. The mature quadratic programming method ensures the efficiency and optimality of control command calculation, ultimately achieving precise, efficient, and overall optimization-compliant drive control for each mover. Furthermore, by constructing distance control matrices and change control matrices related to the system control cycle and dynamic model, a mathematical mapping relationship from the current state and control input to the future state is established. Using these matrices, the current position of the mover is combined with the upcoming control change, and the predicted position is accurately calculated using standard discrete-time state-space equations. This allows the system to pre-assess the potential future consequences of the control command before actual execution, particularly the relative positional relationships between movers, providing crucial forward-looking information for subsequent safety verification and control decisions. In addition, by systematically increasing the load sensitivity factor and subsequently updating the load sensitivity weights, By optimizing a series of key parameters, including operating speed, control changes, and safe operating distance, and then recalculating the predicted position and performing safety verification, the system can proactively and gradually adjust the control strategy until a solution that meets both operational requirements and ensures future operational safety is found. This greatly enhances the system's robustness and safety in complex and dynamic environments, enabling the magnetic drive conveyor system to self-correct and optimize when facing potential collision risks, ultimately achieving safe, reliable, and efficient mover operation control. Furthermore, proportional and differential feedback (forming a PD feedback controller) ensures that the mover can accurately track the predicted position and quickly eliminate deviations. Simultaneously, dynamic feedforward compensation generated by calculating the phase offset based on the current load mass and optimized operating speed, along with subsequent sine processing, proactively adapts to the impact of mover operating condition changes on drive characteristics. The phase compensation current (along with the control changes themselves) formed by combining these feedback and feedforward signals with the basic control changes acts on the stator, achieving precise, stable, and rapid closed-loop control of the mover's motion state. This effectively improves the positioning accuracy, anti-disturbance capability, and overall dynamic performance of the magnetic drive conveyor system in actual operation.

[0247] This application also provides a mover operation control device for a magnetic drive conveyor system, which can implement the above-described mover operation control method for the magnetic drive conveyor system. (Refer to...) Figure 14 The device 1400 includes:

[0248] The data acquisition module 1410 is used to acquire the current load quality of each mover and the current running distance between each pair of adjacent movers;

[0249] The speed optimization module 1420 is used to calculate the optimized running speed of each mover based on the current load quality and the current running interval;

[0250] The position prediction module 1430 is used to calculate the control change of each stator based on the optimized running speed, and to calculate the predicted position of each mover at the prediction time based on the control change.

[0251] The safe distance calculation module 1440 is used to calculate the safe operating distance between each pair of adjacent movers based on the current load quality and optimized operating speed.

[0252] The operation control module 1450 is used to control the stator based on the control change when the predicted operating distance between any two adjacent movers, as indicated by the predicted position, exceeds the safe operating distance, so as to facilitate the operation control of the movers.

[0253] In some embodiments, the speed optimization module 1420 is further configured to:

[0254] Determine the load sensitivity weight for each mover based on its current load quality.

[0255] The running optimization function is obtained by multiplying the ratio of the running variable corresponding to each mover to the current running interval by the corresponding load-sensitive weight.

[0256] Based on the operating variables and current load quality corresponding to each mover, the safety distance variable corresponding to each mover is obtained, and based on the numerical relationship between the current operating distance and the safety distance variable, the safety distance constraint is obtained;

[0257] A runtime optimization model is generated based on the runtime optimization function and safety distance constraints;

[0258] Solve the optimization model to obtain the solution corresponding to the running variables of each mover as the optimized running speed.

[0259] In some embodiments, the speed optimization module 1420 is further configured to:

[0260] Obtain the load sensitivity factor, and calculate the load sensitivity quality based on the product of the load sensitivity factor and the current load quality;

[0261] The load-sensitive weights are obtained based on the exponential processing of load-sensitive quality.

[0262] In some embodiments, the speed optimization module 1420 is further configured to:

[0263] The safety distance constraint is relaxed to obtain the safety distance relaxation constraint;

[0264] The operational optimization model is transformed based on the safety distance relaxation constraint to obtain the penalty constraint operational model;

[0265] Based on the quadratic programming method, the penalty constraint running model is solved, and the solution corresponding to the running variable of each mover is used as the optimized running speed.

[0266] In some embodiments, the location prediction module 1430 is further configured to:

[0267] From multiple stators, determine the stator that corresponds to the current position of each mover as the control stator;

[0268] Obtain the control cost function corresponding to each control stator;

[0269] The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method. The solved control variable parameters are used as the control change of each control stator.

[0270] In some embodiments, the location prediction module 1430 is further configured to:

[0271] The efficiency term weight of each control stator is obtained based on the ratio of the load sensitivity weight of the mover corresponding to each control stator to the current running distance.

[0272] The control cost term is obtained by multiplying the efficiency term weight by the square of the optimized operating parameter, and then adding the smoothing term weight by the square of the control change parameter.

[0273] Based on the control cost terms for all predicted time steps, the control cost function corresponding to the control stator is obtained.

[0274] In some embodiments, the location prediction module 1430 is further configured to:

[0275] Based on the multiple relationship of the control cycle, the distance control matrix and the change control matrix are generated;

[0276] The predicted position of each mover at the predicted time is obtained by multiplying the current position of each mover at the current time by the distance control matrix, and then adding the product of the change control matrix and the control change amount.

[0277] In some embodiments, the safe distance calculation module 1440 is further configured to:

[0278] Of two adjacent moving parts, the moving part that comes later is taken as the braking moving part;

[0279] The reaction distance is obtained by multiplying the optimized operating speed of the brake actuator by the system response time.

[0280] The braking distance is obtained by dividing the square of the optimized operating speed of the brake actuator by the maximum acceleration of the brake actuator.

[0281] Based on the reaction distance, braking distance, and safety margin, the safe operating distance between two adjacent movers is obtained.

[0282] In some embodiments, the operation control module 1450 is further configured to:

[0283] Increase the load sensitivity factor to obtain an updated load sensitivity factor, and update the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain the updated load sensitivity weight;

[0284] The optimized operating speed of each mover and the control change of each stator are updated based on the updated load-sensitive weights. The updated operating safety distance is obtained based on the updated optimized operating speed and the updated load-sensitive weights.

[0285] The updated predicted position of each mover at the predicted time is obtained based on the updated control changes;

[0286] Repeat the above parameter update process until the updated predicted position characterizes the predicted operating distance between every two adjacent movers that exceeds the safe operating distance. Then, control the stator according to the updated control change to facilitate the operation control of the movers.

[0287] In some embodiments, the operation control module 1450 is further configured to:

[0288] Proportional feedback is obtained by multiplying the position difference between the predicted position and the current position of each mover by the proportional gain factor.

[0289] Differential feedback is obtained by multiplying the differential of the position difference of each mover by the differential gain factor.

[0290] The phase offset is obtained based on the current load mass and optimized operating speed of the mover corresponding to each stator;

[0291] The dynamic feedforward compensation is obtained by multiplying the sinusoidal processing of the phase offset by the compensation term amplitude.

[0292] Based on the accumulated values ​​of proportional feedback, differential feedback, and dynamic feedforward compensation, the phase compensation current corresponding to each stator is obtained, and the corresponding stator is controlled based on the phase compensation current and the control change.

[0293] In some embodiments, the operation control module 1450 is further configured to:

[0294] The rated thrust is obtained by multiplying the thrust coefficient by the rated current.

[0295] The command acceleration of each mover is obtained based on the difference between the optimized running speed of each mover and the current running speed at the current moment;

[0296] The thrust ratio is obtained by multiplying the current load mass and commanded acceleration of the mover corresponding to each stator by the rated thrust.

[0297] Based on the arctangent of the thrust ratio, the phase offset corresponding to each stator is obtained.

[0298] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, the specific implementation of the mover operation control device of the magnetic drive conveyor system is basically the same as the specific implementation of the mover operation control method of the magnetic drive conveyor system, and will not be repeated here.

[0299] In this embodiment, the mover operation control device of the magnetic drive conveyor system addresses the problem of track deviation and collisions caused by changes in mover load, which in turn affect processing efficiency. It acquires the current load mass of each mover and the current running distance between adjacent movers in real time, and dynamically calculates the optimal running speed of each mover and the dynamic safe running distance that must be maintained between them based on this real-time information. It also incorporates a mechanism for predicting and verifying the future position of the movers, ensuring that movement control is only executed when the predicted future mover distance meets the dynamically calculated safe distance requirement. This effectively overcomes the operational uncertainty caused by changes in workpiece quality during processing, proactively avoids collision risks that may result from mover track deviation, and significantly improves the safety, stability, and adaptive adjustment capability of multi-motor cooperative operation to dynamic working conditions, thereby ensuring the continuity of the processing flow. This approach ensures continuity and reliability, providing strong support for further improving overall production efficiency and system robustness. Furthermore, by introducing load-sensitive weights to dynamically adjust the importance of different load movers in the optimization objective, and constructing an operation optimization function oriented towards local traffic efficiency, while strictly adhering to safety distance constraints calculated based on real-time load and target speed, the optimized operating speed obtained by solving the operation optimization model can not only be personalized according to the real-time operating conditions of each mover (current load quality and current operating distance), ensuring the safety of multi-motor operation and the system's responsiveness to dynamic changes, but also maximize the overall operating efficiency and performance of the conveying system while ensuring safety. Additionally, it precisely identifies which control stators should drive each mover, ensuring targeted control, and then defines a control cost function for each control stator to guide its behavior.Most importantly, by using the optimized operating speed as the input or objective of the control cost function, and again applying quadratic programming to solve for the control variable parameters of the control cost function, the control change is obtained. This allows the optimized operating speed of the upper-level planning to effectively guide the specific behavior of the lower-level controller. The mature quadratic programming method ensures the efficiency and optimality of control command calculation, ultimately achieving precise, efficient, and overall optimization-compliant drive control for each mover. Furthermore, by constructing distance control matrices and change control matrices related to the system control cycle and dynamic model, a mathematical mapping relationship from the current state and control input to the future state is established. Using these matrices, the current position of the mover is combined with the upcoming control change, and the predicted position is accurately calculated using standard discrete-time state-space equations. This allows the system to pre-assess the potential future consequences of the control command before actual execution, particularly the relative positional relationships between movers, providing crucial forward-looking information for subsequent safety verification and control decisions. In addition, by systematically increasing the load sensitivity factor and subsequently updating the load sensitivity weights, By optimizing a series of key parameters, including operating speed, control changes, and safe operating distance, and then recalculating the predicted position and performing safety verification, the system can proactively and gradually adjust the control strategy until a solution that meets both operational requirements and ensures future operational safety is found. This greatly enhances the system's robustness and safety in complex and dynamic environments, enabling the magnetic drive conveyor system to self-correct and optimize when facing potential collision risks, ultimately achieving safe, reliable, and efficient mover operation control. Furthermore, proportional and differential feedback (forming a PD feedback controller) ensures that the mover can accurately track the predicted position and quickly eliminate deviations. Simultaneously, dynamic feedforward compensation generated by calculating the phase offset based on the current load mass and optimized operating speed, along with subsequent sine processing, proactively adapts to the impact of mover operating condition changes on drive characteristics. The phase compensation current (along with the control changes themselves) formed by combining these feedback and feedforward signals with the basic control changes acts on the stator, achieving precise, stable, and rapid closed-loop control of the mover's motion state. This effectively improves the positioning accuracy, anti-disturbance capability, and overall dynamic performance of the magnetic drive conveyor system in actual operation.

[0300] This application also provides an electronic device, including:

[0301] At least one memory;

[0302] At least one processor;

[0303] At least one program;

[0304] The program is stored in a memory, and the processor executes the at least one program to implement the mover operation control method of the magnetic drive conveyor system described above in this application. The electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), in-vehicle computers, etc.

[0305] Please see Figure 15 , Figure 15 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0306] The processor 1501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0307] The memory 1502 can be implemented in the form of ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1502 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1502 and is called and executed by the processor 1501 to execute the mover operation control method of the magnetic drive conveyor system of this application embodiment.

[0308] The input / output interface 1503 is used to implement information input and output;

[0309] The communication interface 1504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0310] Bus 1505 transmits information between various components of the device (e.g., processor 1501, memory 1502, input / output interface 1503, and communication interface 1504);

[0311] The processor 1501, memory 1502, input / output interface 1503 and communication interface 1504 are connected to each other within the device via bus 1505.

[0312] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the above-described mover operation control method of the magnetic drive conveyor system.

[0313] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0314] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0315] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0316] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0317] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0318] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0319] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0320] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0321] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0322] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0323] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0324] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for controlling the movement of a mover in a magnetically driven conveyor system, characterized in that, The magnetic drive conveying system includes a magnetic drive conveying track and multiple movers, the magnetic drive conveying track includes multiple stators, and the movers run on the magnetic drive conveying track. The method includes: Obtain the current load mass of each of the movers, and obtain the current running distance between each pair of adjacent movers; Based on the current load quality and the current running interval, the optimal running speed of each of the movers is calculated; Based on the optimized operating speed, the control change amount of each stator is calculated, and based on the control change amount, the predicted position of each mover at the predicted time is calculated. Based on the current load quality and the optimized operating speed, the safe operating distance between each pair of adjacent movers is calculated; When the predicted position indicates that the predicted operating distance between any two adjacent movers exceeds the safe operating distance, the stator is controlled based on the control change amount to facilitate the operation control of the movers; The step of calculating the optimized operating speed of each mover based on the current load quality and the current operating interval includes: Determine the load sensitivity weight for each of the movers based on the current load quality of each mover; Based on the ratio of the running variable corresponding to each of the aforementioned actuators to the current running interval, and then multiplied by the corresponding load-sensitive weight, the running optimization function is obtained; Based on the operating variables and the current load quality corresponding to each of the aforementioned actuators, a safety distance variable corresponding to each of the aforementioned actuators is obtained, and a safety distance constraint is obtained based on the numerical relationship between the current operating distance and the safety distance variable. An operational optimization model is generated based on the operational optimization function and the safety distance constraint; Solving the operation optimization model yields the solution corresponding to the operation variable of each actuator, which is then used as the optimized operation speed. Solving the operational optimization model to obtain the solution corresponding to the operational variable of each actuator as the optimized operational speed includes: The safety distance constraint is relaxed to obtain the safety distance relaxation constraint; The operational optimization model is transformed based on the aforementioned safety distance relaxation constraint to obtain a penalty-constrained operational model; Based on the quadratic programming method, the penalty constraint running model is solved to obtain the solution corresponding to the running variable of each mover as the optimized running speed.

2. The method of claim 1, wherein the method further comprises: The determination of the load sensitivity weight corresponding to each mover based on the current load quality of each mover includes: Obtain the load sensitivity factor, and obtain the load sensitivity quality based on the product of the load sensitivity factor and the current load quality; The load-sensitive weight is obtained based on the exponential processing of the load-sensitive quality.

3. The mover operation control method of the magnetic drive conveyor system according to claim 1, characterized in that, The calculation of the control change for each stator based on the optimized operating speed includes: From the multiple stators, determine the stator that is currently in the position of each mover as the corresponding control stator; Obtain the control cost function corresponding to each of the control stators; The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method. The solved control variable parameters are used as the control change amount of each control stator.

4. The method of claim 3, wherein the method further comprises: The step of obtaining the control cost function corresponding to each of the control stators includes: The efficiency term weight of each control stator is obtained based on the ratio of the load sensitivity weight of the actuator corresponding to each control stator to the current operating distance. The control cost term is obtained by multiplying the efficiency term weight and the square of the optimized operating parameter, plus the smoothing term weight and the square of the control change parameter. Based on the control cost terms for all predicted time steps, the control cost function corresponding to the control stator is obtained.

5. The method of claim 1, wherein the method further comprises: The calculation of the predicted position of each mover at the predicted time based on the control change includes: Based on the multiple relationship of the control cycle, the distance control matrix and the change control matrix are generated; The predicted position of each mover at the predicted time is obtained by multiplying the current position of each mover at the current time with the distance control matrix, and adding the product of the change control matrix and the control change amount.

6. The method of claim 1, wherein the method further comprises: The calculation of the safe operating distance between each pair of adjacent movers based on the current load quality and the optimized operating speed includes: Of the two adjacent moving parts, the latter moving part is designated as the braking moving part; The reaction distance is obtained by multiplying the optimized operating speed of the brake actuator and the system response time. The braking distance is obtained by dividing the square of the optimized operating speed of the brake actuator by the maximum acceleration of the brake actuator. Based on the reaction distance, the braking distance, and the safety margin, the safe operating distance between two adjacent movers is obtained.

7. The method of claim 2, wherein the method further comprises: determining a position of the rotor of the magnetic drive conveyor system; and determining a speed of the rotor of the magnetic drive conveyor system. When the predicted position indicates that the predicted operating distance between any two adjacent movers does not exceed the safe operating distance, the method further includes: Increase the load sensitivity factor to obtain an updated load sensitivity factor, and update the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain the updated load sensitivity weight; Based on the updated load-sensitive weights, the optimized operating speed of each of the movers and the control change of each of the stators are updated, and the updated operating safety distance is obtained based on the updated optimized operating speeds and the updated load-sensitive weights. The updated predicted position of each mover at the predicted time is obtained based on the updated control change. The above parameter update process is repeated until the updated predicted position indicates that the predicted operating distance between any two adjacent movers exceeds the safe operating distance. The stator is then controlled according to the updated control change to facilitate the operation control of the movers.

8. The method of claim 1, wherein the method further comprises: determining a position of the rotor of the magnetic drive conveyor system; and determining a speed of the rotor of the magnetic drive conveyor system. The control of the stator based on the control change includes: Proportional feedback is obtained by multiplying the position difference between the predicted position and the current position of each mover by the proportional gain factor. Differential feedback is obtained by multiplying the differential of the position difference of each mover by a differential gain factor. The phase offset is obtained based on the current load mass and the optimized operating speed of the mover corresponding to each stator; Based on the sinusoidal processing of the phase offset, and then multiplied by it to obtain the compensation term amplitude, dynamic feedforward compensation is obtained. Based on the accumulated values ​​of the proportional feedback, the differential feedback, and the dynamic feedforward compensation, the phase compensation current corresponding to each stator is obtained, and the corresponding stator is controlled based on the phase compensation current and the control change amount.

9. The method of claim 8, wherein the method further comprises: determining a position of the rotor based on the magnetic field; and controlling the rotor based on the position of the rotor. The step of obtaining the phase offset based on the current load mass of the mover corresponding to each stator and the optimized operating speed includes: The rated thrust is obtained by multiplying the thrust coefficient by the rated current. The command acceleration of each mover is obtained based on the difference between the optimized running speed of each mover and the current running speed at the current moment; The thrust ratio is obtained by multiplying the current load mass of the mover corresponding to each stator and the commanded acceleration, and then dividing by the rated thrust. Based on the arctangent processing of the thrust ratio, the phase offset corresponding to each stator is obtained.

10. A mover operation control device for a magnetic drive conveyor system, characterized in that, A method for controlling the movement of a mover in a magnetic drive conveyor system as described in claim 1, wherein the magnetic drive conveyor system includes a magnetic drive conveyor track and a plurality of movers, the magnetic drive conveyor track includes a plurality of stators, and the movers run on the magnetic drive conveyor track, the device comprising: The data acquisition module is used to acquire the current load mass of each of the movers and the current running distance between each pair of adjacent movers; The speed optimization module is used to calculate the optimized operating speed of each of the movers based on the current load quality and the current running interval; The position prediction module is used to calculate the control change of each stator based on the optimized running speed, and to calculate the predicted position of each mover at the prediction time based on the control change. The safe distance calculation module is used to calculate the safe operating distance between each pair of adjacent actuators based on the current load quality and the optimized operating speed. The operation control module is used to control the stator based on the control change amount when the predicted operating distance between any two adjacent movers, as indicated by the predicted position, exceeds the safe operating distance, so as to facilitate the operation control of the movers.

11. An electronic device, comprising: The system includes a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the mover operation control method of the magnetic drive conveyor system according to any one of claims 1 to 9.

12. A computer readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the mover operation control method of the magnetic drive conveyor system according to any one of claims 1 to 9.

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