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

Abstract

The mover operation control method of the magnetic driving conveying system and the related equipment provided by the embodiments of the present application, the method comprises: acquiring a current operation parameter corresponding to a current position of a mover at a current time, and acquiring a target operation parameter required for the mover to reach a target position; generating a time-optimal trajectory function based on the current operation parameter and the target operation parameter, and generating a smoothing optimization function based on a jump parameter, the current time and a target time parameter; generating a smoothing time fitness model based on the time-optimal trajectory function and the smoothing optimization function; solving the smoothing time fitness model to obtain an optimized target time, and obtaining an optimized operation parameter corresponding to the operation of the mover from the current position to the target position based on the optimized target time, and performing operation control on the mover based on the optimized operation parameter, so that the generated operation trajectory not only considers the operation efficiency, but also significantly improves the operation smoothness of the mover, and reduces the high-frequency vibration caused by frequent and intense electromagnetic thrust adjustment.

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, appropriate operational planning parameters are typically planned for each mover in advance using relevant kinematic formulas, based on its initial velocity, initial position, and target position, combined with its own operational parameters, to improve the mover's operational efficiency. However, in the magnetic drive operating environment, to achieve the operational control corresponding to the motion planning parameters, the electromagnetic thrust applied to the mover needs to be changed in real time. But this real-time changing electromagnetic thrust results in low smoothness of mover operation, and frequent and excessively large changes in electromagnetic thrust will cause high-frequency vibration of the stator in the magnetic drive conveyor track, leading to overload or heat loss problems in the magnetic drive conveyor system. Summary of the Invention

[0004] This application provides a method and related equipment for controlling the movement of a mover in a magnetic drive conveyor system, which can improve the smoothness of the movement of the mover in the magnetic drive conveyor system and reduce the frequency of electromagnetic thrust changes.

[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 mover, the mover running on the magnetic drive conveyor track. The method includes:

[0006] Obtain the current operating parameters of the mover at its current position at the current moment, and obtain the target operating parameters required for the mover to reach the target position;

[0007] Generate a time-optimal trajectory function based on the current operating parameters and the target operating parameters, and generate a smoothing optimization function based on the jump parameter, the current time, and the target time parameter;

[0008] A smooth time fitness model is generated based on the time-optimal trajectory function and the smoothing optimization function;

[0009] Solving the smoothed time fitness model yields the optimized target time. Based on the optimized target time, the current operating parameters, and the target operating parameters, the optimized operating parameters corresponding to the movement of the mover from the current position to the target position are obtained. The movement of the mover is then controlled based on the optimized operating parameters.

[0010] In some embodiments, generating the time-optimal trajectory function based on the current operating parameters and the target operating parameters includes:

[0011] Based on the kinematic formula between the current operating parameters corresponding to the current time and the target operating parameters corresponding to the target time parameters, the motion constraint matrix function of the mover from the current position to the target position is obtained;

[0012] Based on the motion constraint matrix function, and by minimizing the difference between the target time parameter and the current time, the time-optimal trajectory function is obtained.

[0013] In some embodiments, obtaining the time-optimal trajectory function based on minimizing the difference between the target time parameter and the current time includes:

[0014] Based on the constraints of the operating parameters, generate operating parameter penalty terms;

[0015] Based on the difference between the target time parameter and the current time, a time optimization term is obtained;

[0016] The time penalty term is obtained by summing the time optimization term and the running parameter penalty term.

[0017] The time-optimal trajectory function is obtained by minimizing the time penalty term.

[0018] In some embodiments, generating a smoothing optimization function based on the jerk parameter, the current time, and the target time parameter includes:

[0019] Based on the current time and the target time parameters, the root mean square of the jump parameter is used to obtain a smoothing optimization term;

[0020] The smoothing optimization function is obtained by minimizing the smoothing optimization term.

[0021] In some embodiments, generating a smoothed time fitness model based on the time-optimal trajectory function and the smoothing optimization function includes:

[0022] Obtain smoothing weights and time weights;

[0023] The smoothed time fitness model is obtained by summing the product of the time weight and the time optimization term in the time-optimal trajectory function, the product of the smoothing weight and the smoothing optimization function, and the time penalty term in the time-optimal trajectory function.

[0024] In some embodiments, solving the smoothed time fitness model to obtain the optimization target time includes:

[0025] Based on the target time parameters, multiple initial target times are generated;

[0026] Based on the smooth time fitness model, the fitness corresponding to each initial target time is calculated, and based on the numerical sorting of the fitness, a preset number of initial target times to inherit are selected from multiple initial target times as leader target times, and the remaining initial target times are selected as follower target times.

[0027] Obtain the tangent convergence factor corresponding to the current iteration parameters, and obtain the cooperating coefficient vector based on the tangent convergence factor;

[0028] The follower target time is updated based on the coordination coefficient vector and the leader target time to obtain the updated target time;

[0029] When the fitness of the updated target time is less than the fitness of the leadership target time, the leadership target time is updated based on the updated target time, and the iteration continues until a preset number of iterations is reached. The leadership target time corresponding to the last iteration is then obtained to obtain the optimized target time.

[0030] In some embodiments, obtaining the tangent convergence factor corresponding to the current iteration parameters includes:

[0031] The pi ratio is obtained based on the ratio of pi to the convergence constant factor, and the iteration ratio is obtained based on the ratio of the current iteration parameter to the preset number of iterations.

[0032] Based on the product of the circumference ratio and the iteration ratio, the tangent is then applied, and the result is divided by the tangent of the circumference ratio to obtain the tangent iteration factor.

[0033] The difference between one and the tangent iteration factor is multiplied by two to obtain the tangent convergence factor.

[0034] 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 mover, the mover running on the magnetic drive conveyor track. The device includes:

[0035] The parameter acquisition module is used to acquire the current operating parameters of the mover at its current position at the current moment, and to acquire the target operating parameters required for the mover to reach the target position;

[0036] The optimization function generation module is used to generate a time-optimal trajectory function based on the current running parameters and the target running parameters, and to generate a smoothing optimization function based on the jump parameter, the current time, and the target time parameter;

[0037] The fitness model generation module is used to generate a smoothed time fitness model based on the time-optimal trajectory function and the smoothing optimization function.

[0038] The optimization control module is used to solve the smoothed time fitness model to obtain the optimization target time, and based on the optimization target time, the current running parameters and the target running parameters, to obtain the optimization running parameters corresponding to the movement of the current position of the moving part to the target position, and to control the movement of the moving part based on the optimization running parameters.

[0039] 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.

[0040] 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.

[0041] The present application proposes a method and related equipment 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 mover, with the mover running on the magnetic drive conveyor track. The method includes: first, obtaining the current operating parameters of the mover at its current position and the target operating parameters required for the mover to reach the target position; then, generating a time-optimal trajectory function based on the current operating parameters and the target operating parameters, and generating a smoothing optimization function based on the jump parameter, the current time, and the target time parameter; next, generating a smoothing time fitness model based on the time-optimal trajectory function and the smoothing optimization function; finally, solving the smoothing time fitness model to obtain the optimized target time, and obtaining the optimized operating parameters corresponding to the mover's movement from the current position to the target position based on the optimized target time, the current operating parameters, and the target operating parameters, and controlling the movement of the mover based on the optimized operating parameters. This application's embodiments innovatively construct and solve a "smoothing time fitness model" by obtaining the current and target operating parameters of the mover. This model simultaneously considers the time-optimal trajectory function corresponding to the operating time and the smoothing optimization function corresponding to the operating smoothness. This yields an optimized target time that balances efficiency and stability. Based on the optimized target time, the optimized operating parameters of the mover during operation that balance efficiency and stability are further obtained. This allows the model to proactively seek the optimal balance between operating time and mover judder during the planning stage. The generated operating trajectory not only considers operating efficiency but also significantly improves the smoothness of the mover's operation, reducing high-frequency vibrations caused by frequent and drastic adjustments to electromagnetic thrust. This reduces the risk of overload or excessive heat loss in the magnetic drive conveyor system, making it particularly suitable for industrial automation scenarios such as precision assembly and SMT where high efficiency, stability, and system stability are required.

[0042] 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

[0043] Figure 1 This is a schematic diagram of the structure of a magnetic drive conveying system provided in one embodiment of this application.

[0044] 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.

[0045] Figure 3 yes Figure 2 The flowchart for step 202.

[0046] Figure 4 yes Figure 3The flowchart for step 302.

[0047] Figure 5 yes Figure 2 Another flowchart for step 202.

[0048] Figure 6 yes Figure 2 The flowchart for step 203.

[0049] Figure 7 yes Figure 2 The flowchart for step 204.

[0050] Figure 8 yes Figure 7 The flowchart for step 703.

[0051] Figure 9 This is a simulation diagram illustrating an optimization target time iteration process provided in another embodiment of this application.

[0052] Figure 10 This is a schematic diagram of the structure of the mover operation control device of a magnetic drive conveyor system provided in an embodiment of this application.

[0053] Figure 11 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] In existing technologies, before conveying multiple movers on a magnetic drive conveyor track, appropriate operational planning parameters are typically planned for each mover in advance using relevant kinematic formulas, based on its initial velocity, initial position, and target position, combined with its own operational parameters, to improve the mover's operational efficiency. However, in the magnetic drive operating environment, to achieve the operational control corresponding to the motion planning parameters, the electromagnetic thrust applied to the mover needs to be changed in real time. But this real-time changing electromagnetic thrust results in low smoothness of mover operation, and frequent and excessively large changes in electromagnetic thrust will cause high-frequency vibration of the stator in the magnetic drive conveyor track, leading to overload or heat loss problems in the magnetic drive conveyor system.

[0059] To improve the smoothness of the mover operation in a magnetic drive conveyor system and reduce the frequency of electromagnetic thrust changes, this application innovatively constructs and solves a "smoothing time fitness model" by obtaining the current and target operating parameters of the mover. This model simultaneously considers the time-optimal trajectory function corresponding to the operating time and the smoothness optimization function corresponding to the operating smoothness. This yields an optimized target time that balances efficiency and stability. Based on the optimized target time, the optimized operating parameters of the mover during operation that balance efficiency and stability are further obtained. This allows for proactively seeking the optimal balance between operating time and mover judder during the planning stage. The generated operating trajectory not only considers operating efficiency but also significantly improves the smoothness of the mover operation, reducing high-frequency vibrations caused by frequent and drastic electromagnetic thrust adjustments. This reduces the risk of overload or excessive heat loss in the magnetic drive conveyor system, making it particularly suitable for industrial automation scenarios such as precision assembly and SMT where high efficiency, stability, and system stability are required.

[0060] 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 1As shown, the magnetic drive conveying system includes a magnetic drive conveying track and a mover. The mover runs on the magnetic drive conveying track and moves on the magnetic levitation conveying track. At any given time, the mover is at its current position. In some application scenarios, the mover needs to perform corresponding control operations at the target position. These control operations have precise requirements for the operating data. That is, when the mover runs to the target position, it needs to reach the pre-defined target operating parameters (including speed, acceleration, etc.) so that external equipment (such as processing equipment) can process the workpiece carried on the mover.

[0061] 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 204. It is also understood that this embodiment... Figure 2 The order of steps 201 to 204 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.

[0062] Step 201: Obtain the current operating parameters of the mover at the current position at the current moment, and obtain the target operating parameters required for the mover to reach the target position.

[0063] Step 201 will be described in detail below.

[0064] In some embodiments, in response to planning the operation control of the mover, the current operating parameters corresponding to the current position x0 at the current time t0 of the mover are first obtained. These current operating parameters constitute the initial boundary conditions for the subsequent motion trajectory planning of the mover. They are a set of physical quantities that describe the motion state of the mover at the starting point of the trajectory segment. They typically include at least the instantaneous velocity v0 and instantaneous acceleration a0 of the mover at the current time t0. Together with the current position x0, they completely define the initial motion state of the mover.

[0065] At the same time, it is also necessary to obtain the target position x that the moving part needs to reach. f The target operating parameters are the desired parameters to be achieved. These target operating parameters constitute the terminal boundary conditions for the trajectory planning of the mover, constraining the final state of the trajectory. They represent the requirements for the mover's motion state at the end of the trajectory segment, typically including at least the desired state at the target position x. f The target velocity v that the actuator should possess f and target acceleration a fFor example, in applications requiring precise stopping at a target location, the target velocity and target acceleration are typically set to zero. These acquired current and target operating parameters collectively define the specific motion task range that the mover needs to complete, as well as its start and end state constraints, serving as the foundational input data for subsequent generation and optimization of the mover's trajectory.

[0066] Step 202: Generate the time-optimal trajectory function based on the current running parameters and the target running parameters, and generate a smoothing optimization function based on the jump parameter, the current time, and the target time parameter.

[0067] Step 202 will be described in detail below.

[0068] In some embodiments, after obtaining the current operating parameters of the mover at its current position and the target operating parameters required to reach the target position, these current and target operating parameters are used with kinematic constraints to establish a time-optimal trajectory function aimed at minimizing the mover's running time. This function is directly related to the transport efficiency. Furthermore, to ensure smooth movement of the mover and reduce shocks and vibrations, a smoothing optimization function is constructed based on the jerk parameter (i.e., the rate of change of acceleration (jerk), a key indicator of smoothness) corresponding to the planned trajectory, the known current time, and the target time parameter as the optimization variable. This function aims to quantify and minimize the degree of trajectory irregularity (i.e., maximize smoothness). These two parallel-generated functions represent the optimization requirements for running time and running smoothness, laying the foundation for the next step of integration into a unified optimization model.

[0069] The following section will first describe how to generate a time-optimal trajectory function based on the current operating parameters and the target operating parameters.

[0070] Reference Figure 3 The process involves generating a time-optimal trajectory function based on the current operating parameters and the target operating parameters, including the following steps 301 to 302.

[0071] Step 301: Based on the kinematic formula between the current operating parameters at the current time and the target operating parameters at the target time, obtain the motion constraint matrix function between the mover and the target position.

[0072] Step 302: Based on the motion constraint matrix function, and by minimizing the difference between the target time parameter and the current time, obtain the time-optimal trajectory function.

[0073] Steps 301 to 302 are described in detail below.

[0074] In some embodiments, the current operating parameters (including current position x0, instantaneous velocity v0, and instantaneous acceleration a0) corresponding to the current time t0 obtained in previous steps and the target time parameter t to be determined are used as the basis. f The corresponding target operating parameters (including target position x) f Target velocity v f and target acceleration a f Using the kinematics formulas describing the motion of the mover, the motion constraint matrix function for the mover's movement from its current position to its target position is derived and established. Here, the kinematics formulas typically refer to mathematical models that describe the changes in the mover's displacement, velocity, and acceleration over time, such as commonly used polynomial interpolation functions. By substituting the current and target motion parameters as boundary conditions of this model, a set of equations concerning the model's undetermined coefficients can be obtained.

[0075] That is, the fifth-order polynomial trajectory is used as the operational formula, and its corresponding positional operational formula is shown in the following formula (1).

[0076] q(t)=α0+α1t+α2t 2 +α3t 3 +α4t 4 +α5t 5 (1)

[0077] Where t is the motion time parameter, α i (i = 0, ..., 5) are the runtime planning parameters.

[0078] Next, by taking the first derivative of positional motion formula (1), we can obtain the velocity motion formula of the mover as shown in formula (2) below.

[0079]

[0080] Similarly, by taking the second derivative of positional motion formula (1), we can obtain the motion formula for acceleration as shown in formula (3) below.

[0081]

[0082] Therefore, further based on the current operating parameters (including current position x0, instantaneous velocity v0, and instantaneous acceleration a0) corresponding to the current time t0 and the target time parameter t f The corresponding target operating parameters (including target position x) f Target velocity v f and target acceleration a f ), and the above-mentioned related kinematics formulas, can further obtain the motion constraint matrix function corresponding to the movement of the mover from the current position to the target position as shown in the following formula (4).

[0083]

[0084] The motion constraint matrix function (4) can be further simplified to TB = q, where T is a 6×6 time matrix representing the trajectory, B is a 6×1 coefficient vector of the trajectory, and q represents a 6×1 position vector of the trajectory. Combined with the motion constraint matrix function (4), given t0 and t... f Then the trajectory coefficient matrix B can be obtained through B = T -1 q is calculated.

[0085] It is understandable that the motion constraint matrix function is to express this set of equations (including formulas (1)-(3)) in matrix form, and this matrix function explicitly or implicitly includes the target time parameter t. f This demonstrates the inherent relationship between the start and end state constraints that the motion of the mover must satisfy and the selected kinematic model.

[0086] Furthermore, based on the actual physical output limitations of the underlying driver and the actual operational requirements on site, constraints on the motion trajectory of the mover need to be set: First, the maximum acceleration of the mover is limited by the underlying driver and cannot be set too large; second, the motion trajectory of the mover should have a certain degree of smoothness; finally, to ensure a certain level of efficiency, the speed of the mover cannot be too small. Generally, the jerk j (i.e., jerk) is used to represent the smoothness of the mover's trajectory. The smaller the jerk, the smoother the motion and the better the stability of the motion. Therefore, the constraint conditions of the mover's motion trajectory are shown in the following formula (5).

[0087]

[0088] In the formula, t max v represents the longest time of motion of the mover. max These represent the maximum velocity of the mover; a max j represents the maximum acceleration of the mover; max This indicates the maximum jog of the mover.

[0089] It is understandable that the target time parameter t is obtained by solving the relevant motion optimization model. f After obtaining the corresponding optimized value, the target time parameter t is needed. f Substitute the corresponding optimized value back into the motion constraint matrix function (4) above to determine the running plan parameters {α1, α2, α3, α4, α5}; then, using the obtained running plan parameters {α1, α2, α3, α4, α5}, further combine them with relevant running formulas (including formulas (1)-(3)) to determine the parameter t at the target time. fThe optimized value, combined with the current operating parameters and the target operating parameters, is the operating parameters (including real-time operating velocity v, real-time operating acceleration a, and real-time operating jerk j) corresponding to each time t during the motion trajectory of the mover. These operating parameters need to satisfy the constraint conditions (5) of the motion trajectory of the mover. If they do not satisfy the constraint conditions, it indicates that the target time parameter t is not satisfied. f The optimized values ​​are not applicable and need to be regenerated.

[0090] Next, after obtaining the motion constraint matrix function (4) that reflects the kinematic constraints, we further construct the objective for time optimization, namely the time-optimal trajectory function, as described below.

[0091] Reference Figure 4 Based on minimizing the difference between the target time parameter and the current time, the time-optimal trajectory function is obtained, including the following steps 401 to 404.

[0092] Step 401: Generate runtime parameter penalty terms based on runtime parameter constraints.

[0093] Step 402: Based on the difference between the target time parameter and the current time, obtain the time optimization term.

[0094] Step 403: Based on the sum of the time optimization term and the running parameter penalty term, obtain the time penalty term.

[0095] Step 404: Obtain the time-optimal trajectory function based on minimizing the time penalty term.

[0096] Steps 401 to 404 are described in detail below.

[0097] In some embodiments, in order to ensure that the planned motion trajectory is physically feasible and meets the actual operation requirements, it is necessary to consider the operation parameter constraints imposed on the motion parameters (i.e., the constraints (5) of the motion trajectory mentioned above). Then, the penalty function method is used to further transform the operation parameter constraints (5) into operation parameter penalty terms in the case of violation of the constraints, as shown in the following formula (6).

[0098]

[0099] Among them, f t ,f v ,f a ,f j Let λ represent the time, velocity, acceleration, and jump penalty functions, respectively; t ,λ v ,λ a ,λ jThese represent the weights of the velocity, acceleration, and jerk penalty functions, respectively. It's understandable that the runtime parameter penalty term is a mathematical function designed so that when the calculated result of the mover trajectory satisfies all runtime parameter constraints, the value of this penalty term is zero or very small; however, when any runtime parameter constraint is violated (e.g., velocity exceeds the limit), the value of this penalty term will increase significantly, and generally, the greater the degree of violation, the higher the penalty value. This mechanism means that in subsequent optimization processes, trajectory schemes that violate constraints will be at a disadvantage in evaluation due to their higher penalty values.

[0100] Besides the constraints mentioned above, the polynomial trajectory does not fully meet the actual application scenarios and requirements of magnetic drive conveyor lines. During the movement of the mover, when the range of motion between workstations (i.e., the current position and the target position) is determined, the trajectory running time affects the efficiency of the magnetic drive conveyor line. Therefore, time is a significant factor. Therefore, considering the sum of the entire trajectory running time as an optimization objective, based on the difference between the target time parameter and the current time, the time optimization term is obtained as shown in the following formula (7).

[0101] f T =min(t) f -t0) (7)

[0102] This time optimization term represents a direct pursuit of operational efficiency. The smaller the value, the shorter the time required for the actuator to complete the task, and the higher the efficiency.

[0103] Next, based on the sum of the time optimization term (7) and the running parameter penalty term (8), the time penalty term is obtained as (t f -t0+f t +f v +f a +f j The time-optimal trajectory function is obtained by minimizing the time penalty term, as shown in the following formula (8).

[0104] f T =min(t) f -t0+f t +f v +f a +f j (8)

[0105] Through steps 301 to 302 and steps 401 to 404 above, an efficiency objective centered on minimizing the mover running time (the difference between the target time parameter and the current time) is established. Then, by introducing a running parameter penalty term based on actual running parameter constraints, and combining this penalty term with the time optimization term to form a unified time penalty term, the time-optimal trajectory function is finally defined based on minimizing this time penalty term. This ensures that the optimal time scheme sought is obtained under the premise of fully considering and satisfying the physical and operational constraints of the system. This avoids the problem that pursuing only the theoretical shortest time may lead to the planned trajectory exceeding the hardware capability or safety range. As a result, the generated time-optimal trajectory function not only points to high efficiency, but also guarantees practical feasibility and operational safety, laying a solid foundation for obtaining a fast and stable mover running control scheme in the future.

[0106] The following section will further describe how to generate a smooth optimization function.

[0107] Reference Figure 5 The smooth optimization function is generated based on the jump parameter, the current time and the target time parameter, including the following steps 501 to 502.

[0108] Step 501: Based on the parameters of the current time and the target time, perform root mean square calculation on the jerk parameter to obtain the smoothing optimization term.

[0109] Step 502: Obtain the smoothing optimization function based on minimizing the smoothing optimization term.

[0110] Steps 501 to 502 are described in detail below.

[0111] In some embodiments, besides operating speed, the smoothness of the mover trajectory is also a crucial factor in the mover's motion. In actual production, the mover trajectory is required to be smooth and without abrupt changes to avoid high-frequency vibration of the stator in the magnetic drive conveyor track caused by frequent and excessively changing electromagnetic thrust, thus preventing overload or heat loss in the magnetic drive conveyor system.

[0112] To quantify the smoothness of the mover's trajectory, the current time t0 and the target time parameter t, which is the optimization variable, are used. f The defined time interval is the time interval between the current time and the target time corresponding to the jump parameter j generated by the trajectory throughout the entire motion ([t0,t...). f The root mean square operation is performed to obtain the smoothing optimization term, and the smoothing optimization function is obtained by minimizing the smoothing optimization term as shown in the following formula (9).

[0113]

[0114] This smoothing optimization term can effectively reflect the overall unevenness or vibration tendency of the entire trajectory, thus facilitating the subsequent use of this smoothing optimization function to find a motion trajectory scheme that can suppress jumps to the greatest extent, reduce acceleration abrupt changes, and thus achieve the smoothest operation.

[0115] Through steps 501 to 502 above, by performing root mean square calculation on the jerk parameter (acceleration) over the entire motion time interval defined by the parameters at the current and target times, a smoothing optimization term that can comprehensively reflect the overall smoothness of the trajectory is obtained. A smoothing optimization function is then constructed based on minimizing this smoothing optimization term. This ensures that in the subsequent trajectory planning and solving process, the system will actively seek and tend to select trajectory schemes that can significantly reduce drastic changes in acceleration and reduce system vibration and impact. This directly helps to improve the stability of the mover's operation, protect the precision workpieces on the mover, and reduce the mechanical and electrical impact on the magnetic drive conveyor system itself, thereby improving the stability and reliability of the system.

[0116] Step 203: Generate a smoothed time fitness model based on the time-optimal trajectory function and the smoothing optimization function.

[0117] Step 203 will be described in detail below.

[0118] In some embodiments, after obtaining the time-optimal trajectory function and the smoothing optimization function, a smooth time fitness model that considers both time optimization and smoothing optimization is further generated based on the time-optimal trajectory function and the smoothing optimization function, as described below.

[0119] Reference Figure 6 The smooth time fitness model is generated based on the time-optimal trajectory function and the smooth optimization function, including the following steps 601 to 602.

[0120] Step 601: Obtain the smoothing weights and time weights.

[0121] Step 602: Accumulate the product of time weight and time optimization term in the time-optimal trajectory function, the product of smoothing weight and smoothing optimization function, and the time penalty term in the time-optimal trajectory function to obtain the smoothed time fitness model.

[0122] Steps 601 to 602 are described in detail below.

[0123] In some embodiments, the time and judder of the mover's operation are mutually constrained in actual operation. A decrease in the trajectory's running time leads to an increase in the judder, resulting in an uneven trajectory. Therefore, a balance needs to be struck between these two optimization objectives (including the time-optimal trajectory function (8) and the smoothing optimization function (9)). Thus, a dual-objective time-judder optimal trajectory model, namely the smoothing time fitness model, is proposed. For the time-judder optimal trajectory model, a decrease in judder will lead to an increase in running time. Trajectory planning inevitably encounters such trade-offs between multiple objective functions. Therefore, a weighted coefficient method is adopted to assign a weight to each objective and solve for the weighted optimization objective.

[0124] That is, based on the accumulated time weight K T With the time optimization term (t) in the time-optimal trajectory function f The product of -t0) and the smoothing weight K J With smooth optimization function f J The product of the time-optimal trajectory function and the time penalty term (f) t +f v +f a +f j The smooth time fitness model is obtained as shown in the following formula (10).

[0125]

[0126] Where the time weight K T And smoothing weight K J It can be a pre-determined constant weight value, or it can be a weight value that adapts in real time according to the operational requirements between the current position and the target position (such as the need for smoother operation or faster speed), satisfying K. T +K J =1.

[0127] Because these two optimization objective values ​​may differ by orders of magnitude, the optimization objective may easily favor the larger value. Therefore, weights are needed to balance the difference between the two objectives. The choice of the two weights depends on the actual needs and should be flexibly configured according to the different operating conditions and environments of the conveyor line. Generally, in environments such as filling, the smoothness of the trajectory is emphasized, and K... T Choosing a smaller value results in a smoother trajectory but a longer running time. However, when trajectory smoothness is less critical and operational efficiency is more important, K... T Choosing a larger value results in a shorter trajectory running time and a faster movement speed.

[0128] Through steps 601 to 602 above, by utilizing explicit smoothing weights and time weights, an adjustable control mechanism is provided for balancing the two competing optimization objectives (i.e., running time and running smoothness). This allows for different degrees of emphasis on efficiency or stability depending on the specific application scenario. The time weight is multiplied by the core objective, i.e., the time optimization term, in the time-optimal trajectory function, and the smoothing weight is multiplied by the smoothing optimization function representing the smoothness. The weighted results of these two items are then accumulated with the time penalty term in the time-optimal trajectory function that ensures physical constraints are met. Finally, a single, comprehensive smoothing time fitness model is constructed, transforming the originally complex multi-objective optimization problem (simultaneously optimizing time and reducing hops while satisfying constraints) into a single-objective optimization problem. This single fitness model not only inherently includes weighted considerations for time and smoothness but also embeds penalties for running parameter constraints, allowing subsequent optimization algorithms to directly solve this model. This leads to a mover operation control scheme that achieves the optimal trade-off between running time and running smoothness under the set weights and while satisfying all constraints.

[0129] Step 204: Solve the smooth time fitness model to obtain the optimized target time, and based on the optimized target time, the current running parameters and the target running parameters, obtain the optimized running parameters for the mover to run from the current position to the target position, and control the movement of the mover based on the optimized running parameters.

[0130] Step 204 will be described in detail below.

[0131] In some embodiments, after obtaining the smooth time fitness model, a corresponding optimization algorithm (such as the Grey Wolf optimization algorithm or other iterative optimization methods) is further used to solve the constructed smooth time fitness model in order to find the specific target time parameter value that enables its function value to reach the optimum (i.e., the minimum value of the smooth time fitness model (10) under the kinematic constraints). The optimal target time parameter value is the optimization target time. It represents the target position x reached by the target mover when the optimal balance between time and operational smoothness is achieved, while satisfying all constraints and weight settings. f The corresponding optimal time.

[0132] Next, based on this calculation, the optimization objective time is... And combining the current operating parameters (including current position x0, instantaneous velocity v0, and instantaneous acceleration a0) at the determined current time t0 with the target operating parameters (including target position x0, instantaneous velocity v0, and instantaneous acceleration a0)... f Target velocity v f and target acceleration a fSubstituting these determined boundary conditions and time parameters into the preset operational formulas (i.e., formulas (1)-(4) above), the system accurately calculates the state that can connect the starting point and the ending point, and optimizes the time from the current time t0 to the target time. The complete trajectory parameters for the motion within the magnetic drive conveyor system (including the real-time running speed v, real-time running acceleration a, and real-time running jerk j at each time t) are calculated. These parameters, which fully define the position, velocity, acceleration, and even jolt of the mover at every moment during the entire motion process, constitute the optimized running parameters for the mover to move from the current position to the target position. Finally, these optimized running parameters are input into the motion controller of the magnetic drive conveyor system. The controller generates real-time control commands (such as drive current or voltage signals) based on these parameters, driving the mover to precisely follow the planned optimized trajectory on the magnetic drive conveyor track, balancing efficiency and stability, thereby completing the controlled motion from the current position to the target position.

[0133] The following will further describe how to solve the smoothed time fitness model using the improved Grey Wolf optimization algorithm of the embodiments of this application.

[0134] Reference Figure 7 Solving the smoothed time fitness model to obtain the optimization target time includes the following steps 701 to 705.

[0135] Step 701: Generate multiple initial target times based on the target time parameters.

[0136] Step 702: Based on the smooth time fitness model, calculate the fitness corresponding to each initial target time, and select a preset number of initial target times from multiple initial target times based on the numerical ranking of fitness as the leader target time, and use the remaining initial target times as the follower target times.

[0137] Step 703: Obtain the tangent convergence factor corresponding to the current iteration parameter, and obtain the co-coefficient vector based on the tangent convergence factor.

[0138] Steps 701 to 703 are described in detail below.

[0139] In some embodiments, the Grey Wolf Optimization (GWO) algorithm is inspired by the pack hunting behavior of grey wolves. The GWO algorithm is characterized by having few parameters to adjust, a relatively simple structure, and low computational cost, making it easy to find the optimal solution in a short time.

[0140] The Grey Wolf optimization algorithm mainly consists of four stages: exploring prey (exploration), surrounding prey, hunting, and attacking prey (development). Global optimization is achieved through the update and iteration of these four stages, and the optimal solution is finally obtained. The mathematical model of the Grey Wolf algorithm can be shown in the following formula (11).

[0141] D = CX p (n)-X(n)

[0142] X(n+1)=X p (n)-A·D (11)

[0143] Where D is the current distance between the wolf pack and the prey, and X(n) and X(n+1) are the current and updated wolf pack position vectors, respectively; X p (n) is the current position vector of the leading wolf pack; A and C are the coordination coefficient vectors, as shown in the following formula (12).

[0144] A = a(2r1 - 1)

[0145] C = 2r² (12)

[0146] Where r1 and r2 are random numbers between 0 and 1, and a is the convergence factor, generally as shown in the following formula (13).

[0147]

[0148] However, in the traditional Grey Wolf optimization algorithm, the convergence factor a is updated iteratively using the linear decreasing strategy shown in formula (13). Different update strategies for this parameter can greatly affect the performance of the algorithm, and the linear strategy is often not the most efficient. As can be seen from formulas (11) and (12), the value of |A| is determined by the convergence factor a. a decreases linearly from 2 to 0 throughout the iteration process. The rate of change of the convergence factor is the same throughout the iteration process. If the convergence speed is too fast in the early stage of the iteration, it will lead to a small search range and insufficient population diversity. If the convergence speed is too slow in the later stage of the iteration, it will lead to low algorithm solution efficiency. Therefore, this application proposes a convergence factor update method based on the tangent function, which is described in detail below.

[0149] Reference Figure 8 To obtain the tangent convergence factor corresponding to the current iteration parameters, the steps 801 to 803 are as follows.

[0150] Step 801: Obtain the circumference ratio based on the ratio of pi to the convergence constant factor, and obtain the iteration ratio based on the ratio of the current iteration parameter to the preset number of iterations.

[0151] Step 802: Based on the product of the circumference ratio and the iteration ratio, perform tangent processing, and then divide by the tangent value of the circumference ratio to obtain the tangent iteration factor.

[0152] Step 803: Based on the difference between one and the tangent iteration factor, multiply by two to obtain the tangent convergence factor.

[0153] Steps 801 to 803 are described in detail below.

[0154] In some embodiments, during each iteration, the circumference ratio π / c is obtained based on the ratio of pi (a) to the convergence constant factor c > 2, and the current iteration parameter n and the preset iteration number N are used as the basis for this process. max The ratio of n / N is used to obtain the iterative ratio. max Then, based on the product of the circumference ratio and the iteration ratio, perform tangent processing, and then divide by the tangent of the circumference ratio to obtain the tangent iteration factor tan((π / c)·(n / N)). max )) / tan(π / c); Based on the difference between one and the tangent iteration factor, multiply by two to obtain the tangent convergence factor corresponding to the current iteration parameter n as shown in the following formula (14).

[0155]

[0156] Then, this tangent convergence factor is used to replace the convergence factor in the traditional Grey Wolf optimization algorithm, so that the cooperative coefficient vector is updated in each iteration to update the parameters. Based on the above improved Grey Wolf optimization algorithm, the process of solving the smooth time fitness model is further described below.

[0157] To initiate the solution process for the smooth time fitness model, the preset number of iterations N for this solution algorithm iteration is first determined. max And the number of gray wolves N, etc., and then, based on the target time parameter t to be optimized f Based on feasible ranges or prior knowledge, multiple different initial target times are generated. These initial target times constitute the starting solution set of the optimization algorithm, and each initial target time represents a potential, possible target running time t. f Candidate values. By generating multiple such initial points, the diversity of the search space can be increased, which helps to prevent the algorithm from getting trapped in local optima too early.

[0158] Next, each generated initial target time is evaluated, that is, based on the previously established smooth time fitness model (10), the target value f corresponding to each initial target time is calculated as the corresponding fitness value. Fitness is a quantitative indicator that measures the quality of the initial target time as a solution (in this scheme, the smaller the fitness value, the better the initial target time). Subsequently, based on these calculated fitness values, they are sorted, and a preset number of initial target times (for example, in the gray wolf optimization algorithm, the three best are usually selected) are selected from multiple initial target times and designated as leader target times (these represent the best or better solutions found so far, used to guide the search direction); it can be understood that in the gray wolf optimization algorithm, the leader target times corresponding to each iteration parameter include the best target time, the second best target time, and the third best target time, which correspond to the best, second best, and third best target times in the initial target times (or updated target times) of this iteration parameter; at the same time, the remaining initial target times are classified as follower target times (these solutions will be updated and explored following the guidance of the leader target times).

[0159] Subsequently, in order to control the search behavior of the optimization algorithm during the iteration process, in each iteration, based on the tangent convergence factor (14) corresponding to the current iteration parameter n, the above formula (12) is used to further obtain the coordination coefficient vector (i.e. A and C, which determine the degree of influence and randomness of the leader on the follower's position update).

[0160] Step 704: Update the follower target time based on the synergy coefficient vector and the leader target time to obtain the updated target time.

[0161] Step 705: When the fitness of the updated target time is less than the fitness of the leader target time, update the leader target time based on the updated target time, continue iterating until the preset number of iterations is reached, and obtain the leader target time corresponding to the last iteration to get the optimized target time.

[0162] Steps 704 to 705 are described in detail below.

[0163] In each iteration, after obtaining the coordination coefficient vector corresponding to the current iteration parameter n and the leader target time, the position update calculation is performed for each following target time using the above formula (11). It can be understood that each leader target time (such as the optimal target time, the second-best target time, and the third-best target time) is taken as the current leader wolf pack position vector X. p(n) Position update calculations are performed to guide the following target times. This update process simulates the behavior of individuals learning from and moving towards better individuals in swarm intelligence algorithms. The goal is to guide these following target times to move towards a better solution region, thereby generating a new set of candidate solutions. These newly generated solutions are called the updated target times.

[0164] In each iteration, after obtaining the updated target time, the fitness of each newly generated updated target time is calculated again using the smoothed time fitness model (11). When the fitness of an updated target time is found to be better than (i.e., the fitness value is less than) the fitness of a current leader target time, the corresponding leader target time is updated (i.e., replaced) based on this better updated target time. After all updates and replacements are completed, it is checked whether the preset iteration termination condition has been met, i.e., whether the number of iterations has reached the preset number of iterations. If not, the next round of update iterations continues (including obtaining new convergence factors, updating followers, evaluating and updating leaders); if the preset number of iterations has been reached, the iteration process terminates. At this time, the optimal leader target time (i.e., the leader with the smallest fitness) maintained after the last iteration cycle is obtained, and it is used as the final output result of the entire optimization process, i.e., the optimized target time is obtained.

[0165] Reference Figure 9 This is a simulation diagram illustrating an optimization of the target time iteration process provided in an embodiment of this application. Figure 9 As shown in the figure, the horizontal axis represents the number of iterations of the optimization algorithm (corresponding to the current iteration parameter n changing from 0 to the preset number of iterations N). max =500), with the vertical axis representing the change in fitness values. The graph contains four curves: the solid line ("linear") represents the linear convergence factor used in the traditional Grey Wolf optimization algorithm, whose value decreases linearly from 2 to 0 uniformly with the number of iterations. The other three curves correspond to different convergence constant factors (c=5, c=3, c=2.1). From Figure 9As can be seen, the tangent convergence factor proposed in this embodiment exhibits a non-linear decreasing trend compared to the traditional linear convergence factor. Specifically, in the early stages of iteration, the tangent convergence factor decreases more slowly than the linear factor, causing the value of 'a' to remain relatively large for a longer period. However, in the later stages of iteration, its decreasing rate is faster than the linear factor, rapidly approaching 0. This non-linear characteristic, especially the slow decrease in the early stages, helps enhance the algorithm's global search (exploration) capability in the early stages, giving the algorithm more opportunities to escape local optima. The rapid decrease in the later stages helps the algorithm to achieve rapid convergence and local fine-grained search (utilization) after finding potential optimal regions. Furthermore, by adjusting the value of the convergence constant factor 'c' (as shown in the figure, c=5, c=3, c=2.1), the specific shape of this non-linear decreasing curve can be changed, thus providing a more flexible exploration-utilization balance adjustment mechanism for the optimization process. This aims to improve the efficiency of solving the smooth time fitness model and the accuracy of finding the optimization target time.

[0166] Through steps 701 to 705 and steps 801 to 803, multiple initial target times are generated, and the leader and follower target times are evaluated and selected based on a smoothed time fitness model. This lays the foundation for swarm intelligence search and helps avoid getting trapped in local optima. Furthermore, a unique tangent convergence factor calculation method is introduced. This method uses a specific tangent function calculated based on pi, convergence constant, current iteration parameters, and a preset iteration count. Compared to the traditional linearly decreasing convergence factor, this non-linear convergence factor, dynamically adjusted based on the tangent function, can more precisely control the global search of the algorithm during the iteration process. The balance between search and local fine search is further improved by using a synergistic coefficient vector obtained from the improved tangent convergence factor to guide the updating of the target time. Combined with the elite retention strategy, the algorithm continues to iterate until the termination condition is met. This iterative solution mechanism, which includes the calculation of a specific tangent convergence factor, aims to improve the convergence speed and optimization accuracy of the optimization algorithm, enhance its ability to escape local optimum traps, and thus more likely to accurately find the optimization target time that makes the smooth time fitness model reach the global optimum (or close to the global optimum). Ultimately, it ensures that the obtained mover running scheme achieves the best balance between time and running smoothness under the premise of satisfying constraints.

[0167] This application proposes a method and related equipment 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 mover, with the mover running on the magnetic drive conveyor track. The method includes: first, obtaining the current operating parameters of the mover at its current position and the target operating parameters required for the mover to reach a target position; then, based on the kinematic formula between the current operating parameters and the target operating parameters, obtaining the motion constraint matrix function between the mover's current position and the target position; and generating an operating parameter penalty term based on the motion constraint matrix function and the operating parameter constraints of the operating parameters. The time optimization term is obtained from the difference between the target time parameter and the current time. A time penalty term is obtained by accumulating the time optimization term and the running parameter penalty term. The optimal time trajectory function is obtained by minimizing the time penalty term. A smoothing optimization term is obtained by taking the root mean square of the jump parameter based on the current and target time parameters. A smoothing optimization function is obtained by minimizing the smoothing optimization term. Next, smoothing weights and time weights are obtained. The product of the time weights and the time optimization term in the optimal time trajectory function, the product of the smoothing weights and the smoothing optimization function, and the time penalty term in the optimal time trajectory function are accumulated to obtain the smoothed time fitness model. Finally, based on the target time... The algorithm generates multiple initial target times based on the time fitness model. It calculates the fitness of each initial target time and selects a predetermined number of initial target times as the leader target times based on the fitness values, ranking them as follows. The remaining initial target times are used as follower target times. The algorithm calculates the pi ratio based on the ratio of pi to the convergence constant factor, and the iteration ratio based on the ratio of the current iteration parameter to the predetermined number of iterations. The product of the pi ratio and the iteration ratio is then tangented and divided by the tangent of the pi ratio to obtain the tangent iteration factor. The difference between the pi ratio and the tangent iteration factor is then calculated. Multiply by two to obtain the tangent convergence factor. Based on the tangent convergence factor, obtain the coordination coefficient vector. Based on the coordination coefficient vector and the leader target time, update the follower target time to obtain the updated target time. When the fitness of the updated target time is less than the fitness of the leader target time, update the leader target time based on the updated target time and continue iterating until the preset number of iterations is reached. Obtain the leader target time corresponding to the last iteration to obtain the optimized target time. Based on the optimized target time, the current running parameters, and the target running parameters, obtain the optimized running parameters corresponding to the mover moving from the current position to the target position. Then, control the mover's operation based on the optimized running parameters.

[0168] This application's embodiments innovatively construct and solve a "smooth time fitness model" by obtaining the current and target operating parameters of the mover. This model simultaneously considers the time-optimal trajectory function corresponding to the operating time and the smooth optimization function corresponding to the operating smoothness. This yields an optimized target time that balances efficiency and stability. Based on this target time, further optimized operating parameters that balance efficiency and stability during the mover's operation are obtained. This allows for proactively seeking the optimal balance between operating time and mover judder during the planning stage. The generated operating trajectory not only considers operating efficiency but also significantly improves the mover's operating smoothness, reducing high-frequency vibrations caused by frequent and drastic electromagnetic thrust adjustments. This reduces the risk of overload or excessive heat loss in the magnetic drive conveyor system, making it particularly suitable for industrial automation scenarios such as precision assembly and SMT where high efficiency, stability, and system stability are required. Furthermore, an efficiency objective is established with minimizing the mover's operating time (the difference between the target time parameter and the current time). An operating parameter penalty term based on actual operating parameter constraints is introduced and combined with the time optimization term to form a unified time penalty term. The optimal time trajectory function is defined by minimizing the time penalty term, ensuring that the optimal time scheme is derived under the premise of fully considering and satisfying the physical and operational constraints of the system. This avoids the problem that pursuing only the theoretical shortest time may lead to the planned trajectory exceeding the hardware capability or safety range. The generated optimal time trajectory function not only points to high efficiency but also guarantees practical feasibility and operational safety, laying a solid foundation for obtaining a fast and stable mover operation control scheme. In addition, by using the root mean square calculation of the jerk parameter (acceleration) over the entire motion time interval defined by the parameters of the current time and the target time, a smoothing optimization term that can comprehensively reflect the overall smoothness of the trajectory is obtained. The smoothing optimization function is constructed based on minimizing this smoothing optimization term. This ensures that in the subsequent trajectory planning and solving process, it will actively seek and tend to select those trajectory schemes that can significantly reduce drastic changes in acceleration and reduce system vibration and impact. This directly helps to improve the smoothness of the mover operation, protect the precision workpieces on the mover, and reduce the mechanical and electrical impact on the magnetic drive conveyor system itself, thereby improving the stability and reliability of the system.Furthermore, by utilizing explicit smoothing weights and time weights, an adjustable control mechanism is provided for balancing two competing optimization objectives (i.e., running time and running smoothness). This allows for different degrees of emphasis on efficiency or stability depending on the specific application scenario. The time weight is multiplied by the core objective, i.e., the time optimization term, in the time-optimal trajectory function, and the smoothing weight is multiplied by the smoothing optimization function representing the smoothness. The weighted results of these two items are then accumulated with the time penalty term in the time-optimal trajectory function that ensures physical constraints are met. Finally, a single, comprehensive smoothing time fitness model is constructed, transforming the originally complex multi-objective optimization problem (simultaneously optimizing time and reducing hops while satisfying constraints) into a single-objective optimization problem. This single fitness model not only inherently includes weighted considerations for time and smoothness but also embeds penalties for running parameter constraints, allowing subsequent optimization algorithms to directly solve this model. This leads to finding a mover operation control scheme that achieves the optimal trade-off between running time and running smoothness under the set weights and while satisfying all constraints. Additionally, by generating multiple... The initial target time, evaluated and selected based on the smoothed time fitness model, lays the foundation for swarm intelligence search and helps avoid getting trapped in local optima. A unique tangent convergence factor calculation method is then introduced, obtained through a specific tangent function calculation based on pi, convergence constant, current iteration parameters, and a preset iteration count ratio. Compared to the traditional linearly decreasing convergence factor, this non-linear, dynamically adjusted convergence factor based on the tangent function can more precisely control the balance between global search and local refinement during the iteration process. Furthermore, the synergy coefficient vector obtained based on this improved tangent convergence factor guides the update of the follower target time, and combined with an elite retention strategy, it iterates continuously until the termination condition is met. This iterative solution mechanism, which includes the calculation of a specific tangent convergence factor, aims to improve the convergence speed and optimization accuracy of the optimization algorithm, enhance its ability to escape local optima, and thus more accurately find the optimization target time that makes the smoothed time fitness model reach global optimum (or near global optimum). Ultimately, it ensures that the obtained mover operation scheme achieves the best balance between time and operational smoothness while satisfying constraints.

[0169] 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 10 The device 1000 includes:

[0170] The parameter acquisition module 1010 is used to acquire the current operating parameters of the mover at the current position at the current moment, and to acquire the target operating parameters required for the mover to reach the target position;

[0171] The optimization function generation module 1020 is used to generate the time-optimal trajectory function based on the current running parameters and the target running parameters, and to generate a smooth optimization function based on the jump parameter, the current time, and the target time parameter.

[0172] The fitness model generation module 1030 is used to generate a smooth time fitness model based on the time-optimal trajectory function and the smooth optimization function.

[0173] The optimization control module 1040 is used to solve the smooth time fitness model to obtain the optimization target time, and based on the optimization target time, the current running parameters and the target running parameters, to obtain the optimization running parameters corresponding to the movement of the current position of the mover to the target position, and to control the movement of the mover based on the optimization running parameters.

[0174] In some embodiments, the optimization function generation module 1020 is further configured to:

[0175] Based on the kinematic formula between the current operating parameters at the current time and the target operating parameters at the target time, the motion constraint matrix function between the mover and the target position is obtained.

[0176] Based on the motion constraint matrix function, and by minimizing the difference between the target time parameter and the current time, the time-optimal trajectory function is obtained.

[0177] In some embodiments, the optimization function generation module 1020 is further configured to:

[0178] Based on the constraints of the operating parameters, generate operating parameter penalty terms;

[0179] Based on the difference between the target time parameter and the current time, the time optimization term is obtained;

[0180] The time penalty term is obtained by summing the time optimization term and the running parameter penalty term.

[0181] The time-optimal trajectory function is obtained by minimizing the time penalty term.

[0182] In some embodiments, the optimization function generation module 1020 is further configured to:

[0183] Based on the parameters at the current time and the target time, the root mean square of the jerk parameter is used to obtain the smoothing optimization term;

[0184] The smoothing optimization function is obtained by minimizing the smoothing optimization term.

[0185] In some embodiments, the fitness model generation module 1030 is further configured to:

[0186] Obtain smoothing weights and time weights;

[0187] By summing the product of the time weights and the time optimization term in the time-optimal trajectory function, the product of the smoothing weights and the smoothing optimization function, and the time penalty term in the time-optimal trajectory function, we obtain the smoothed time fitness model.

[0188] In some embodiments, the optimization control module 1040 is further configured to:

[0189] Based on the target time parameters, multiple initial target times are generated;

[0190] Based on the smooth time fitness model, the fitness corresponding to each initial target time is calculated, and the initial target time with a preset number of inheritances is selected from multiple initial target times based on the numerical ranking of fitness as the leader target time, and the remaining initial target times are selected as follower target times.

[0191] Obtain the tangent convergence factor corresponding to the current iteration parameters, and obtain the coherence coefficient vector based on the tangent convergence factor;

[0192] The updated target time is obtained by updating the follower target time based on the synergy coefficient vector and the leader target time;

[0193] When the fitness of the updated target time is less than that of the leader target time, the leader target time is updated based on the updated target time, and the iteration continues until the preset number of iterations is reached. The leader target time corresponding to the last iteration is then obtained to obtain the optimized target time.

[0194] In some embodiments, the optimization control module 1040 is further configured to:

[0195] The pi ratio is obtained based on the ratio of pi to the convergence constant factor, and the iteration ratio is obtained based on the ratio of the current iteration parameter to the preset number of iterations.

[0196] Based on the product of the circumference ratio and the iteration ratio, the tangent is then applied, and finally divided by the tangent of the circumference ratio to obtain the tangent iteration factor;

[0197] The difference between one and the tangent iteration factor is multiplied by two to obtain the tangent convergence factor.

[0198] 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.

[0199] In this embodiment, the mover operation control device of the magnetic drive conveyor system innovatively constructs and solves a "smooth time fitness model" that simultaneously considers the time-optimal trajectory function corresponding to the running time and the smooth optimization function corresponding to the running smoothness by acquiring the current and target running parameters of the mover. This model obtains an optimized target time that balances efficiency and stability. Based on the optimized target time, it further obtains optimized running parameters of the mover that balance efficiency and stability during operation. This allows the system to proactively seek the optimal balance between running time and mover judder during the planning stage. The generated running trajectory not only considers running efficiency but also significantly improves the running smoothness of the mover, reducing high-frequency vibrations caused by frequent and drastic electromagnetic thrust adjustments. This reduces the risk of overload or excessive heat loss in the magnetic drive conveyor system, making it particularly suitable for industrial automation scenarios such as precision assembly and SMT where high efficiency, stability, and system stability are required. Furthermore, an efficiency objective is established with minimizing the mover running time (the difference between the target time parameter and the current time). An operating parameter penalty term based on actual running parameter constraints is introduced and combined with the time optimization term to form a unified... The time penalty term is used to define the optimal time trajectory function, which is ultimately minimized. This ensures that the optimal time scheme is derived under the premise of fully considering and satisfying the physical and operational constraints of the system. This avoids the problem that pursuing only the theoretical shortest time may lead to the planned trajectory exceeding the hardware capability or safety range. The generated optimal time trajectory function not only points to high efficiency but also guarantees practical feasibility and operational safety, laying a solid foundation for obtaining a fast and stable mover operation control scheme. In addition, by using the root mean square calculation of the jerk parameter (acceleration) over the entire motion time interval defined by the parameters of the current time and the target time, a smoothing optimization term that can comprehensively reflect the overall smoothness of the trajectory is obtained. The smoothing optimization function is constructed based on minimizing this smoothing optimization term. This ensures that in the subsequent trajectory planning and solving process, it will actively seek and tend to select those trajectory schemes that can significantly reduce drastic changes in acceleration and reduce system vibration and impact. This directly helps to improve the smoothness of the mover operation, protect the precision workpieces on the mover, and reduce the mechanical and electrical impact on the magnetic drive conveyor system itself, thereby improving the stability and reliability of the system.Furthermore, by utilizing explicit smoothing weights and time weights, an adjustable control mechanism is provided for balancing two competing optimization objectives (i.e., running time and running smoothness). This allows for different degrees of emphasis on efficiency or stability depending on the specific application scenario. The time weight is multiplied by the core objective, i.e., the time optimization term, in the time-optimal trajectory function, and the smoothing weight is multiplied by the smoothing optimization function representing the smoothness. The weighted results of these two items are then accumulated with the time penalty term in the time-optimal trajectory function that ensures physical constraints are met. Finally, a single, comprehensive smoothing time fitness model is constructed, transforming the originally complex multi-objective optimization problem (simultaneously optimizing time and reducing hops while satisfying constraints) into a single-objective optimization problem. This single fitness model not only inherently includes weighted considerations for time and smoothness but also embeds penalties for running parameter constraints, allowing subsequent optimization algorithms to directly solve this model. This leads to finding a mover operation control scheme that achieves the optimal trade-off between running time and running smoothness under the set weights and while satisfying all constraints. Additionally, by generating multiple... The initial target time, evaluated and selected based on the smoothed time fitness model, lays the foundation for swarm intelligence search and helps avoid getting trapped in local optima. A unique tangent convergence factor calculation method is then introduced, obtained through a specific tangent function calculation based on pi, convergence constant, current iteration parameters, and a preset iteration count ratio. Compared to the traditional linearly decreasing convergence factor, this non-linear, dynamically adjusted convergence factor based on the tangent function can more precisely control the balance between global search and local refinement during the iteration process. Furthermore, the synergy coefficient vector obtained based on this improved tangent convergence factor guides the update of the follower target time, and combined with an elite retention strategy, it iterates continuously until the termination condition is met. This iterative solution mechanism, which includes the calculation of a specific tangent convergence factor, aims to improve the convergence speed and optimization accuracy of the optimization algorithm, enhance its ability to escape local optima, and thus more accurately find the optimization target time that makes the smoothed time fitness model reach global optimum (or near global optimum). Ultimately, it ensures that the obtained mover operation scheme achieves the best balance between time and operational smoothness while satisfying constraints.

[0200] This application also provides an electronic device, including:

[0201] At least one memory;

[0202] At least one processor;

[0203] At least one program;

[0204] 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.

[0205] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0206] The processor 1101 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.

[0207] The memory 1102 can be implemented in the form of ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1102 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 1102 and is called and executed by the processor 1101 to execute the mover operation control method of the magnetic drive conveyor system of this application embodiment.

[0208] Input / output interface 1103 is used to implement information input and output;

[0209] The communication interface 1104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0210] Bus 1105 transmits information between various components of the device (e.g., processor 1101, memory 1102, input / output interface 1103, and communication interface 1104);

[0211] The processor 1101, memory 1102, input / output interface 1103 and communication interface 1104 are connected to each other within the device via bus 1105.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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 a mover, the mover running on the magnetic drive conveying track, and the mover operation control method of the magnetic drive conveying system includes: Obtain the current operating parameters of the mover at its current position at the current moment, and obtain the target operating parameters required for the mover to reach the target position; Generate a time-optimal trajectory function based on the current operating parameters and the target operating parameters, and generate a smoothing optimization function based on the jump parameter, the current time, and the target time parameter; A smooth time fitness model is generated based on the time-optimal trajectory function and the smoothing optimization function; Solving the smoothed time fitness model yields the optimized target time. Based on the optimized target time, the current operating parameters, and the target operating parameters, the optimized operating parameters corresponding to the movement of the mover from the current position to the target position are obtained. The movement of the mover is then controlled based on the optimized operating parameters. The function for generating the time-optimal trajectory based on the current operating parameters and the target operating parameters includes: Based on the kinematic formula between the current operating parameters corresponding to the current time and the target operating parameters corresponding to the target time parameters, the motion constraint matrix function of the mover from the current position to the target position is obtained; Based on the motion constraint matrix function, and by minimizing the difference between the target time parameter and the current time, the time-optimal trajectory function is obtained.

2. The mover operation control method of the magnetic drive conveyor system according to claim 1, characterized in that, The step of obtaining the time-optimal trajectory function based on minimizing the difference between the target time parameter and the current time includes: Based on the constraints of the operating parameters, generate operating parameter penalty terms; Based on the difference between the target time parameter and the current time, a time optimization term is obtained; The time penalty term is obtained by summing the time optimization term and the running parameter penalty term. The time-optimal trajectory function is obtained by minimizing the time penalty term.

3. The mover operation control method of the magnetic drive conveyor system according to claim 1, characterized in that, The generation of the smoothing optimization function based on the jerk parameter, the current time, and the target time parameter includes: Based on the current time and the target time parameters, the root mean square of the jump parameter is used to obtain a smoothing optimization term; The smoothing optimization function is obtained by minimizing the smoothing optimization term.

4. The mover operation control method of the magnetic drive conveyor system according to claim 1, characterized in that, The generation of a smoothed time fitness model based on the time-optimal trajectory function and the smoothing optimization function includes: Obtain smoothing weights and time weights; The smoothed time fitness model is obtained by summing the product of the time weight and the time optimization term in the time-optimal trajectory function, the product of the smoothing weight and the smoothing optimization function, and the time penalty term in the time-optimal trajectory function.

5. The mover operation control method of the magnetic drive conveyor system according to claim 1, characterized in that, The process of solving the smoothed time fitness model to obtain the optimization target time includes: Based on the target time parameters, multiple initial target times are generated; Based on the smooth time fitness model, the fitness corresponding to each initial target time is calculated, and based on the numerical sorting of the fitness, a preset number of initial target times to inherit are selected from multiple initial target times as leader target times, and the remaining initial target times are selected as follower target times. Obtain the tangent convergence factor corresponding to the current iteration parameters, and obtain the cooperating coefficient vector based on the tangent convergence factor; The follower target time is updated based on the coordination coefficient vector and the leader target time to obtain the updated target time; When the fitness of the updated target time is less than the fitness of the leadership target time, the leadership target time is updated based on the updated target time, and the iteration continues until a preset number of iterations is reached. The leadership target time corresponding to the last iteration is then obtained to obtain the optimized target time.

6. The mover operation control method of the magnetic drive conveyor system according to claim 5, characterized in that, The step of obtaining the tangent convergence factor corresponding to the current iteration parameters includes: The pi ratio is obtained based on the ratio of pi to the convergence constant factor, and the iteration ratio is obtained based on the ratio of the current iteration parameter to the preset number of iterations. Based on the product of the circumference ratio and the iteration ratio, the tangent is then applied, and the result is divided by the tangent of the circumference ratio to obtain the tangent iteration factor. The difference between one and the tangent iteration factor is multiplied by two to obtain the tangent convergence factor.

7. A mover operation control device for a magnetic drive conveyor system, characterized in that, The mover operation control device of the magnetic drive conveyor system is used to execute the mover operation control method of the magnetic drive conveyor system as described in any one of claims 1 to 6. The magnetic drive conveyor system includes a magnetic drive conveyor track and a mover, the mover running on the magnetic drive conveyor track. The mover operation control device of the magnetic drive conveyor system includes: The parameter acquisition module is used to acquire the current operating parameters of the mover at its current position at the current moment, and to acquire the target operating parameters required for the mover to reach the target position; The optimization function generation module is used to generate a time-optimal trajectory function based on the current running parameters and the target running parameters, and to generate a smoothing optimization function based on the jump parameter, the current time, and the target time parameter; The fitness model generation module is used to generate a smoothed time fitness model based on the time-optimal trajectory function and the smoothing optimization function. The optimization control module is used to solve the smoothed time fitness model to obtain the optimization target time, and based on the optimization target time, the current running parameters and the target running parameters, to obtain the optimization running parameters corresponding to the movement of the mover from the current position to the target position, and to control the movement of the mover based on the optimization running parameters.

8. An electronic device, characterized in that, The system includes a memory and a processor, wherein 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 according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, 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 6.

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