An adaptive control method, device and equipment of a permanent magnet linear synchronous motor and a medium

Abstract

This application provides an adaptive control method, device, equipment, and medium for a permanent magnet linear synchronous motor, belonging to the field of permanent magnet linear synchronous motor control technology. The method involves: solving a first control law in real time based on motor dynamic characteristic data, motion characteristic data, error characteristic quantities, and a motion error convergence model. This first control law indicates the basic adjustment amount for controlling the motion error convergence of the mover when there is no disturbance, achieving rapid error convergence. Based on the error characteristic quantities and compensation function, compensation coefficients are generated in real time and smoothed using a saturation function to derive a second control law, which provides smooth and effective compensation when disturbances exist. Unlike existing technologies, this application can decouple multiple different control laws through the calculation of the first and second control laws, accommodating control requirements under different operating conditions and ensuring both motor control accuracy and system stability and robustness.

Description

Technical Field

[0001] This application relates to the field of permanent magnet linear synchronous motor control technology, and in particular to an adaptive control method, device, equipment and medium for a permanent magnet linear synchronous motor. Background Technology

[0002] When performing adaptive parameter control on a permanent magnet linear synchronous motor, the control strategy can be automatically adjusted based on the motor's real-time operating status, such as position error, speed, acceleration, load changes, and parameter drift, so that the motor can maintain high precision, high stability, and good dynamic performance under different operating conditions.

[0003] Traditional permanent magnet linear synchronous motor control technology generally employs a control structure with a single control variable. For example, it relies solely on a PID control algorithm based on a fixed model to generate a single drive signal. The control law design is relatively simple and struggles to simultaneously meet the control requirements under different operating conditions. For instance, under conditions of no disturbance or stable parameters, traditional control methods can achieve a certain degree of error convergence by adjusting the control gain. However, when the motor is subjected to external disturbances (such as sudden load changes, friction fluctuations, or end effects), the single control variable often cannot compensate in a timely and effective manner, leading to increased position errors and decreased trajectory tracking accuracy.

[0004] Therefore, when faced with time-varying parameters, external disturbances, and complex dynamic characteristics, traditional technologies struggle to ensure both control accuracy and system stability and robustness. Summary of the Invention

[0005] The main purpose of this application is to propose an adaptive control method, device, equipment and medium for a permanent magnet linear synchronous motor. By calculating the first control law and the second control law, multiple different control laws can be decoupled to take into account the control requirements under different working conditions, ensuring the motor control accuracy while also taking into account the stability and robustness of the system.

[0006] To achieve the above objectives, one aspect of this application proposes an adaptive control method for a permanent magnet linear synchronous motor, the method comprising: Acquire the motor dynamic characteristic data of the permanent magnet linear synchronous motor and the motion characteristic data of the mover at the current moment, and generate an error characteristic quantity to indicate the change law of position error based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment. The error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data are substituted into the motion error convergence model to solve for the first control law; wherein, the motion error convergence model is used to indicate the correlation between the motor's dynamic characteristics, the reference trajectory function of the mover, the motion state of the mover, and the motion trajectory control quantity of the mover; the first control law is used to indicate the basic adjustment amount of the motion state that controls the convergence of the motion error of the mover when there is no disturbance. Based on the error characteristics and the compensation function, compensation coefficients are generated; wherein, the compensation coefficients are used to quantify the motion state compensation intensity required by the mover to counteract the disturbance; the compensation function is used to describe the relationship between the compensation coefficients and the convergence degree of the mover's motion error when a disturbance exists; The second control law is solved based on the compensation coefficient and the saturation function used to suppress the degree of system chattering during the motion trajectory control process of the mover; wherein, the second control law is used to indicate the motion state compensation adjustment amount for controlling the convergence of the motion error of the mover when there is a disturbance. Based on the first control law and the second control law, a target control law is determined, and then a driving signal is generated according to the target control law to control the motion state of the mover at the next moment.

[0007] Preferably, the error characteristic quantity includes: a first error characteristic quantity and a second error characteristic quantity; The step of generating an error feature quantity to indicate the pattern of position error change based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment includes: Based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment, a position tracking error is generated; The time dimension derivative of the position tracking error is calculated to generate a first error feature quantity that characterizes the direction of error change as the position error changes over time. The first error feature is differentiated in the time dimension to generate a second error feature that characterizes the error change trend of position error over time.

[0008] Preferably, the process of constructing the motion error convergence model includes: Obtain the first error calculation function to calculate the direction of position error change over time; Based on the terminal sign function corresponding to the first error calculation function and the fractional integral operator, a fractional integral term is constructed; wherein, the fractional integral term is used to characterize the historical cumulative information of the position error; Based on the fractional integral term and the first error calculation function, a reference trajectory function is constructed; wherein, the reference trajectory function is used to indicate the reference motion trajectory when the motion error of the mover converges to zero. The fractional integral term in the reference trajectory function is differentiated to generate a fractional differential term; wherein, the fractional differential term is used to characterize the variation characteristics of the position error. The first error calculation function in the reference trajectory function is differentiated to generate the second error calculation function; wherein, the second error calculation function is used to calculate the error change trend of position error over time; Based on the relationship between the second error calculation function, the first variable indicating the motor dynamic characteristics, the second variable indicating the mover motion characteristics, and the third variable indicating the motion state adjustment amount, as well as the fractional differential term and the disturbance term, a motion error convergence model is constructed. The fractional derivative term corresponds to an error gain coefficient, which is used to quantify the degree of influence of the fractional derivative term on the motion of the mover to achieve the reference motion trajectory. The disturbance term includes: a fourth variable indicating the change in the total mass of the mover, a fifth variable indicating the change in the coefficient of viscous friction, a sixth variable indicating the total disturbance force of the nonlinear disturbance, and a seventh variable indicating the electromagnetic thrust coefficient. The first variable includes: a first sub-variable indicating the electromagnetic thrust coefficient, a second sub-variable indicating the total mass of the mover, and a third sub-variable indicating the viscous friction coefficient; the second variable includes: a fourth sub-variable indicating the mover acceleration and a fifth sub-variable indicating the mover velocity.

[0009] Preferably, the motor dynamic characteristic data includes: electromagnetic thrust coefficient, total mass of the mover, and viscous friction coefficient; the motion characteristic data includes: mover acceleration and mover velocity. The step of substituting the error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data into the motion error convergence model to solve for the first control law includes: The error characteristic quantity is used as the function value of the first error calculation function. The electromagnetic thrust coefficient, the total mass of the mover, and the viscous friction coefficient are used as the values ​​of the first, second, and third sub-variables, respectively. The mover acceleration and the mover velocity are used as the values ​​of the fourth and fifth sub-variables, respectively. The value of the disturbance term is set to zero, and a convergence model of the motion error to be solved is constructed. Under the preset motion error convergence constraint, the third variable used to indicate the motion state adjustment amount in the motion error convergence model to be solved is solved, and the value corresponding to the third variable is used as the first control law; wherein, the motion error convergence constraint is used to indicate that the function value of the motion error convergence model to be solved is zero.

[0010] Preferably, the second error calculation function includes: an eighth variable for indicating the desired acceleration, a ninth variable for indicating the mover velocity, a tenth variable for indicating the current component of the stator current on the q-axis, a first sub-variable, a second sub-variable, a third sub-variable, and a disturbance term; The process of constructing the compensation function includes: Obtain a function term that indicates the smooth correlation between the compensation strength and the degree of error deviation; Based on the baseline trajectory function, the second error calculation function, the first error calculation function, the function term, the eleventh variable used to indicate the adaptive compensation parameter, and the twelfth variable used to indicate the compensation intensity adjustment parameter, a compensation function is constructed. The adaptive compensation parameter is used to quantify the maximum compensation requirement of the current disturbance; the adaptive compensation parameter is positively correlated with the degree of deviation of the motion trajectory of the mover from the reference motion trajectory when a disturbance exists.

[0011] Preferably, the first error calculation function includes: ; in, This represents the function value of the first error calculation function. For velocity error, it is used to indicate the direction of position error change over time; Indicates the expected velocity of the mover. This indicates the actual velocity of the mover; The second error calculation function includes: ; ; in, This represents the function value of the function expression for calculating the second error. This refers to acceleration error, used to indicate the trend of position error change over time. Indicates the expected acceleration. Indicates actual acceleration. The coefficient of viscous friction, For the total mass of the mover, This is the electromagnetic thrust coefficient. This represents the current component of the stator current on the q-axis. For the disturbance term, This represents the change in the total mass of the mover. This is the change in the coefficient of viscous friction. The total disturbance force of the nonlinear disturbance; The reference trajectory function includes: ; in, Let be the function value of the reference trajectory function, representing the reference trajectory when the motion error of the mover converges to zero. Describes a fractional integral operator. The first error calculation function is expressed as follows. The corresponding terminal symbol function, Represents a fractional integral term. These are the weighting coefficients corresponding to the fractional integral terms. Used to quantify fractional integral terms The extent of the impact; , It is a fractional order; , The exponent of the terminal symbol function; The motion error convergence model includes: ; ; in, for The corresponding first derivative, The function value of the motion error convergence model is used to represent the degree to which the motion trajectory of the mover deviates from the reference motion trajectory; This represents the fractional differential term, used to characterize the variation of position error. The coefficient of viscous friction, For the total mass of the mover, This is the electromagnetic thrust coefficient. This is the first control law.

[0012] Preferably, the compensation function includes: ; ; ; in, Let be the function value of the compensation function, and represent the compensation coefficient. For function terms, To compensate for adaptive parameters, To compensate for the intensity adjustment parameters, for The corresponding time first derivative; For adaptive rate, used to adjust The update speed; The preset leakage coefficient is used to prevent... Unlimited growth; This is the preset upper limit value for the adaptive compensation parameters.

[0013] To achieve the above objectives, another aspect of this application proposes an adaptive control device for a permanent magnet linear synchronous motor, the device comprising: The data acquisition module is used to acquire the motor dynamic characteristic data of the permanent magnet linear synchronous motor and the motion characteristic data of the mover at the current moment, and generate error characteristic quantities to indicate the change law of position error based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment. The first control law generation module is used to substitute the error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data into the motion error convergence model to solve for the first control law; wherein, the motion error convergence model is used to indicate the correlation between the motor's dynamic characteristics, the reference trajectory function of the mover, the motion state of the mover, and the motion trajectory control quantity of the mover; the first control law is used to indicate the basic adjustment amount of the motion state that controls the convergence of the motion error of the mover when there is no disturbance. The compensation coefficient generation module is used to generate compensation coefficients based on the error characteristic quantity and the compensation function; wherein, the compensation coefficient is used to quantify the motion state compensation intensity required by the mover to offset the disturbance; the compensation function is used to describe the relationship between the compensation coefficient and the convergence degree of the mover motion error when a disturbance exists; The second control law generation module is used to solve for the second control law based on the compensation coefficient and the saturation function used to suppress the degree of system chattering during the motion trajectory control process of the mover; wherein, the second control law is used to indicate the motion state compensation adjustment amount for controlling the convergence of the motion error of the mover when there is a disturbance. The control module is used to determine the target control law based on the first control law and the second control law, and then generate a drive signal based on the target control law to control the motion state of the mover at the next moment.

[0014] Another 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 aforementioned adaptive control method for a permanent magnet linear synchronous motor.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned adaptive control method for a permanent magnet linear synchronous motor.

[0016] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned adaptive control method for a permanent magnet linear synchronous motor.

[0017] The embodiments of this application include at least the following beneficial effects: This application provides an adaptive control method, device, equipment, and medium for a permanent magnet linear synchronous motor. The solution first acquires the motor dynamic characteristic data, the motion characteristic data of the mover at the current moment, and the error characteristic quantity of the mover. Then, based on the motor dynamic characteristic data, motion characteristic data, error characteristic quantity, and motion error convergence model, a first control law is solved in real time. This first control law indicates the basic adjustment amount of the motion state controlling the convergence of the mover's motion error when there is no disturbance, thereby achieving rapid error convergence. Furthermore, this application can also generate compensation coefficients in real time based on the error characteristic quantity and the compensation function, and perform smoothing through a saturation function to derive a second control law. This second control law provides smooth and effective compensation when disturbances exist. Therefore, this application can quickly respond and perform appropriate compensation when the motor system is subjected to disturbances, effectively suppressing the impact of disturbances on motor motion. Simultaneously, the saturation function can avoid chattering caused by over-compensation, ensuring the system maintains good stability and robustness under disturbance conditions. Finally, this application generates a target control law by fusing the first and second control laws, enabling the system to automatically adjust its control strategy according to real-time operating conditions. For example, in the absence of disturbances, the first control law ensures error convergence; when disturbances exist, the second control law automatically intervenes to compensate, allowing the motor system to maintain high-precision trajectory tracking capability even when parameters change over time and external disturbances occur. Thus, this application can decouple multiple different control laws through the calculation of the first and second control laws to take into account the control requirements under different operating conditions, ensuring both motor control accuracy and system stability and robustness. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an adaptive control method for a permanent magnet linear synchronous motor provided in an embodiment of this application. Figure 2 This is a block diagram of the permanent magnet linear synchronous motor control device system based on a high-end CNC machine tool provided in the embodiments of this application; Figure 3This is a schematic diagram of the structure of an adaptive control device for a permanent magnet linear synchronous motor provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] 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 of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0020] Figure 1 This is an optional flowchart of an adaptive control method for a permanent magnet linear synchronous motor provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S1 to S5: Step S1: Obtain the motor dynamic characteristic data of the permanent magnet linear synchronous motor and the motion characteristic data of the mover at the current moment, and generate an error characteristic quantity to indicate the change law of position error based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment. Step S2: Substitute the error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data into the motion error convergence model to solve for the first control law; wherein, the motion error convergence model is used to indicate the correlation between the motor's dynamic characteristics, the reference trajectory function of the mover, the motion state of the mover, and the motion trajectory control quantity of the mover; the first control law is used to indicate the basic adjustment amount of the motion state for controlling the convergence of the motion error of the mover when there is no disturbance; Step S3: Generate compensation coefficients based on the error characteristic quantity and the compensation function; wherein, the compensation coefficients are used to quantify the motion state compensation intensity required by the mover to counteract the disturbance; the compensation function is used to describe the relationship between the compensation coefficients and the degree of convergence of the mover motion error when a disturbance exists; Step S4: Solve for the second control law based on the compensation coefficient and the saturation function used to suppress the degree of system chattering during the motion trajectory control process of the mover; wherein, the second control law is used to indicate the motion state compensation adjustment amount for controlling the convergence of the motion error of the mover when there is a disturbance. Step S5: Determine the target control law based on the first control law and the second control law, and then generate a driving signal based on the target control law to control the motion state of the mover at the next moment.

[0021] Steps S1 to S5 as shown in the embodiments of this application, by first acquiring motor dynamic characteristic data and motion characteristic data and generating error characteristic quantities, can achieve accurate perception of the real-time operating status of the motor and provide reliable data basis for solving subsequent control quantities.

[0022] By substituting the error characteristic quantity, dynamic characteristic data, and motion characteristic data into the motion error convergence model, the first control law is obtained. Since the motion error convergence model indicates the correlation between the motor's dynamic characteristics, the reference trajectory function of the mover, the motion state of the mover, and the motion trajectory control quantity of the mover, it comprehensively considers the motor's dynamic characteristics, error convergence law, and motion state, so that the obtained first control law can accurately drive the mover to move along the desired trajectory, thereby improving the basic control accuracy and dynamic response speed of the motor.

[0023] Furthermore, by generating compensation coefficients based on error characteristics and the compensation function in step S3, dynamic identification and quantification of the impact of disturbances can be achieved. The second control law is then solved using the compensation coefficients and the saturation function, effectively reducing system chattering while suppressing disturbances. Since the saturation function limits the compensation intensity, it avoids abrupt changes in control input due to over-compensation, resulting in a smoother compensation process and improved system stability and reliability.

[0024] Finally, the first and second control laws are fused to generate the target control law, which drives the actuator to move, thus achieving the synergistic effect of basic control and compensation control. Illustratively, in this embodiment of the invention, high-precision tracking can be ensured by relying on the first control law when there is no disturbance; when a disturbance exists, the second control law automatically intervenes to compensate. Therefore, through the decoupling calculation of the first and second control laws and the fusion of dual control quantities, this application enables the system to adaptively adjust the control strategy according to real-time operating conditions, taking into account control accuracy, dynamic response, stability, and robustness, thereby improving the overall control performance of the permanent magnet linear synchronous motor.

[0025] Understandably, traditional technologies struggle to achieve rapid and stable error convergence when faced with time-varying parameters and complex dynamic characteristics due to fixed control models and single control variables. However, the first control law of this invention is solved in real-time based on motor dynamic characteristic data, motion characteristic data, and error characteristic quantities. The motion error convergence model dynamically reflects the real-time state of the motor, enabling the first control law to adaptively adjust according to changes in motor parameters and motion state. Combined with the disturbance compensation effect of the second control law, this invention maintains high-precision trajectory tracking capability even under time-varying parameters and complex dynamic environments.

[0026] Traditional technologies often fail to compensate for external disturbances (such as sudden load changes, friction fluctuations, and end effects) effectively and promptly with a single control variable, leading to increased position errors and decreased trajectory tracking accuracy. However, the second control law of this invention generates compensation coefficients in real time based on error characteristics and smooths them using a saturation function. This allows the system to respond quickly and provide appropriate compensation when subjected to disturbances, effectively suppressing the impact of disturbances on motor motion. Simultaneously, the saturation function avoids chattering caused by over-compensation, ensuring the system maintains good stability and robustness under disturbed conditions.

[0027] Therefore, this invention enables the motor system to maintain high precision, high stability and good dynamic performance under various operating conditions, thereby effectively solving the problem that traditional technologies struggle to balance control precision, stability and robustness.

[0028] For step S1, in some embodiments, the error characteristic quantity includes: a first error characteristic quantity and a second error characteristic quantity; The step of generating an error feature quantity to indicate the pattern of position error change based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment includes: Based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment, a position tracking error is generated; The time dimension derivative of the position tracking error is calculated to generate a first error feature quantity that characterizes the direction of error change as the position error changes over time. The first error feature is differentiated in the time dimension to generate a second error feature that characterizes the error change trend of position error over time.

[0029] Indicatively, position tracking error reflects the degree of deviation between the current position of the mover and the target position, and is the most basic error information. Taking the derivative of the position tracking error over time yields the rate of change of the error, which is the first error characteristic quantity. It characterizes the direction of position error change over time, thus indicating whether the error is increasing or decreasing.

[0030] The second derivative of the first error characteristic quantity is obtained by taking the time dimension again, which is the rate of change of the error rate. This second derivative is the second error characteristic quantity, which can characterize the change of the error trend, such as whether the error rate is accelerating or slowing down, and whether the error has a divergent or convergent trend.

[0031] It is understood that the embodiments of this application can extract information at three levels—error magnitude, error direction, and error trend—from the position error, forming a complete error characteristic quantity system. Therefore, in the presence of disturbances, time-varying parameters, or complex dynamic characteristics, the error trend often reflects the true state of the system better than the error itself. Since the solutions to the first and second control laws both depend on error characteristic quantities, and the embodiments of this application introduce the error direction and trend, the calculation of control quantities can be made more accurate, thereby improving the overall control accuracy and stability.

[0032] For step S2, in some embodiments, the present invention can first construct a motion error convergence model. After constructing the motion error convergence model, the error characteristic quantity, motor dynamic characteristic data and motion characteristic data obtained at the current moment can be substituted into the motion error convergence model in real time to solve the first control law.

[0033] In a preferred embodiment, the motion error convergence model of the present invention can quantify the degree to which the motion trajectory of the mover deviates from the reference motion trajectory. The construction process of the motion error convergence model specifically includes: Obtain the first error calculation function to calculate the direction of position error change over time; Based on the terminal sign function corresponding to the first error calculation function and the fractional integral operator, a fractional integral term is constructed; wherein, the fractional integral term is used to characterize the historical cumulative information of the position error; Based on the fractional integral term and the first error calculation function, a reference trajectory function is constructed; wherein, the reference trajectory function is used to indicate the reference motion trajectory when the motion error of the mover converges to zero. The fractional integral term in the reference trajectory function is differentiated to generate a fractional differential term; wherein, the fractional differential term is used to characterize the variation characteristics of the position error. The first error calculation function in the reference trajectory function is differentiated to generate the second error calculation function; wherein, the second error calculation function is used to calculate the error change trend of position error over time; Based on the relationship between the second error calculation function, the first variable indicating the motor dynamic characteristics, the second variable indicating the mover motion characteristics, and the third variable indicating the motion state adjustment amount, as well as the fractional differential term and the disturbance term, a motion error convergence model is constructed. The fractional derivative term corresponds to an error gain coefficient, which is used to quantify the degree of influence of the fractional derivative term on the motion of the mover to achieve the reference motion trajectory. The disturbance term includes: a fourth variable indicating the change in the total mass of the mover, a fifth variable indicating the change in the coefficient of viscous friction, a sixth variable indicating the total disturbance force of the nonlinear disturbance, and a seventh variable indicating the electromagnetic thrust coefficient. The first variable includes: a first sub-variable indicating the electromagnetic thrust coefficient, a second sub-variable indicating the total mass of the mover, and a third sub-variable indicating the viscous friction coefficient; the second variable includes: a fourth sub-variable indicating the mover acceleration and a fifth sub-variable indicating the mover velocity.

[0034] In illustrative terms, the motion error convergence model constructed in this embodiment of the invention can integrate error dynamic information, motor body characteristics, and fractional-order operator characteristics to achieve accurate definition and error convergence control of the mover's reference motion trajectory.

[0035] Specifically, the first error calculation function in this embodiment of the invention is a basic mathematical expression describing the direction of position error change over time. The variable is the real-time position tracking error of the mover, which can be directly associated with the first error feature quantity generated in step S1 and is one of the inputs for model construction and model solving.

[0036] The terminal sign function is used to determine the positive or negative direction of the calibration error. In some embodiments, the terminal sign function can clearly indicate whether the mover is biased to the left or right of the desired position, thereby providing directional constraints for the integral term. In this embodiment of the invention, a fractional integral operator is also employed. Unlike traditional integer integrals, this operator can handle non-integer integral operations and accurately characterize the historical accumulation information of position errors. Therefore, the fractional integral term constructed by combining the terminal sign function and the fractional integral operator can reflect the directional attributes and historical accumulation effects of the error.

[0037] Furthermore, by fusing the first error calculation function, which indicates the direction of real-time error change, with the fractional integral term, which indicates the accumulated error history, a reference trajectory function can be constructed. This function defines the reference motion trajectory when the mover's motion error converges to zero, thus clearly defining the ideal trajectory equation that the mover must follow. Therefore, the reference trajectory function ensures that the motor control process always prioritizes zero error.

[0038] In illustrative terms, embodiments of the present invention can further differentiate the reference trajectory function, and the first derivative of the reference trajectory function is the motion error convergence model. When differentiating the reference trajectory function, the fractional integral term and the first error calculation function in the reference trajectory function are actually differentiated respectively, so as to obtain the final motion error convergence model.

[0039] Specifically, taking the derivative of the fractional integral term essentially transforms the historical accumulation information of the error into the characteristics of the error's change, while the fractional differential term can characterize the degree of influence of the historical accumulation error on the rate of change of the error at the current moment, reflecting the dynamic evolution of the error from the past to the present. Taking the time-dimension derivative of the first error calculation function yields the second error calculation function, which corresponds to the second error feature generated in step S1 and is also one of the inputs for model construction and solution. The purpose of this second error calculation function is to quantify the rate of error change, determining whether the error is accelerating or decelerating, or converging at an accelerating or decelerating pace.

[0040] In one embodiment, a motion error convergence model can be constructed by using the relationship between the second error calculation function, the first variable indicating the motor dynamic characteristics, the second variable indicating the mover motion characteristics, and the third variable indicating the motion state adjustment amount, as well as the fractional derivative term and the disturbance term.

[0041] It is not difficult to see that in the process of constructing the motion error convergence model of the present invention, the introduction of fractional integral terms and differential terms enables the model to not only capture the error direction and trend at the current moment, but also take into account the cumulative effect of historical errors. This avoids the problem of error convergence lag caused by ignoring historical information in traditional models, and makes the model's perception of error changes more comprehensive and accurate.

[0042] The motion error convergence model construction process also incorporates variables related to core dynamic parameters such as the indicative electromagnetic thrust coefficient, mover mass, and viscous friction coefficient, as well as motion parameters such as acceleration and velocity. This makes the motion error convergence model no longer a theoretical model detached from the actual motor, but a practical model highly matched to the characteristics of the motor itself. Even if the motor parameters change over time, such as changes in the electromagnetic coefficient due to temperature, the motion error convergence model of this embodiment can adjust the output based on real-time dynamic characteristic data, improving system robustness. Illustratively, the preset error gain coefficient can be flexibly adjusted according to different application scenarios, such as high-precision CNC machine tools, to quantify the influence weight of the fractional derivative term, enabling the model to adapt to error convergence requirements under different operating conditions.

[0043] Since the error convergence function directly defines the reference trajectory for the error to return to zero, the first control law solved subsequently always aims to track this reference trajectory, avoiding the problem of chaotic control output caused by the lack of a clear reference in traditional models, and ensuring the speed and stability of error convergence under undisturbed conditions.

[0044] The introduction of fractional operators expands the dynamic representation capability of the model. Compared with traditional integer integrals, this invention can more accurately describe the dynamic characteristics of complex nonlinear systems through fractional operators, which is suitable for the strong coupling and nonlinear characteristics of permanent magnet linear synchronous motors, and further improves the model's representation accuracy of the motor's motion state.

[0045] In a preferred embodiment, the first error calculation function includes: ; in, This represents the function value of the first error calculation function. For velocity error, it is used to indicate the direction of position error change over time; Indicates the expected velocity of the mover. This indicates the actual velocity of the mover; The second error calculation function includes: ; ; in, This represents the function value of the function expression for calculating the second error. This refers to acceleration error, used to indicate the trend of position error change over time. Indicates the expected acceleration. Indicates actual acceleration. The coefficient of viscous friction, For the total mass of the mover, This is the electromagnetic thrust coefficient. This represents the current component of the stator current on the q-axis. For the disturbance term, This represents the change in the total mass of the mover. This is the change in the coefficient of viscous friction. The total disturbance force of the nonlinear disturbance; The reference trajectory function includes: ; in, Let be the function value of the reference trajectory function, representing the reference trajectory when the motion error of the mover converges to zero. Describes a fractional integral operator. The first error calculation function is expressed as follows. The corresponding terminal symbol function, Represents a fractional integral term. These are the weighting coefficients corresponding to the fractional integral terms. Used to quantify fractional integral terms The extent of the impact; , It is a fractional order; , The exponent of the terminal symbol function; The motion error convergence model includes: ; ; in, for The corresponding first derivative, The function value of the motion error convergence model is used to represent the degree to which the motion trajectory of the mover deviates from the reference motion trajectory; This represents the fractional differential term, used to characterize the variation of position error. The coefficient of viscous friction, For the total mass of the mover, This is the electromagnetic thrust coefficient. This is the first control law.

[0046] In a preferred embodiment, the motor dynamic characteristic data obtained in step 1 includes: electromagnetic thrust coefficient, total mass of the mover, and viscous friction coefficient; the motion characteristic data obtained in step 1 includes: mover acceleration and mover velocity. Then, substituting the error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data into the motion error convergence model to solve for the first control law includes: The error characteristic quantity is used as the function value of the first error calculation function. The electromagnetic thrust coefficient, the total mass of the mover, and the viscous friction coefficient are used as the values ​​of the first, second, and third sub-variables, respectively. The mover acceleration and the mover velocity are used as the values ​​of the fourth and fifth sub-variables, respectively. The value of the disturbance term is set to zero, and a convergence model of the motion error to be solved is constructed. Under the preset motion error convergence constraint, the third variable used to indicate the motion state adjustment amount in the motion error convergence model to be solved is solved, and the value corresponding to the third variable is used as the first control law; wherein, the motion error convergence constraint is used to indicate that the function value of the motion error convergence model to be solved is zero.

[0047] Indicatively, after substituting the error characteristic quantity, motor dynamic characteristic data, and motion characteristic data at the current moment into the variables corresponding to the constructed motion error convergence model, the motion error convergence model will be transformed into a solution model adapted to the current working condition. At this time, the input of the solution model is the current actual state of the motor, rather than fixed preset parameters.

[0048] The function value of the model to be solved is zero, which is the mathematical equivalent condition for the error to converge to zero. Using this constraint as the solution condition, the essence is to find the first control law that enables the system to meet the goal of error convergence in the specific model under the current working condition. Therefore, through mathematical solution, the first control law that enables the current error characteristics, motor characteristics, and motion state to meet the error convergence condition can be obtained.

[0049] In a preferred embodiment, the reference trajectory function of the present invention is a fractional-order terminal sliding surface. The corresponding function, sliding surface The corresponding trajectory is actually the sliding surface motion trajectory indicated by the reference trajectory function when the error converges to zero.

[0050] Furthermore, regarding the sliding surface By differentiating and substituting into the dynamic equations of the motor system, the dynamics of the sliding surface can be correlated with the actual motion state of the motor, the influence of the control law on the convergence of the sliding surface can be clarified, and thus a motion error convergence model can be constructed.

[0051] Specifically, the prerequisite for constructing a motion error convergence model is to clarify the dynamic characteristics of the motor. Firstly, modeling and field-oriented control can be based on the dq axis. The permanent magnet pole axis is taken as the d-axis, and a 90° electrical angle leading the d-axis is taken as the q-axis, forming a dq coordinate system. Let the d-axis current component of the inner current loop be... Then the electromagnetic thrust equation is ; In the formula, Electromagnetic thrust; The polar distance; For permanent magnet flux linkage; , , , These represent the current and inductance along the d and q axes, respectively; using If the mover current and stator current are controlled to be orthogonal in space, the electromagnetic thrust equation simplifies to: ;in, The electromagnetic thrust coefficient is given by the electromagnetic thrust equation. The linear relationship between electromagnetic thrust and q-axis current can be clearly defined.

[0052] To quantify the relationship between the motion state of the mover and electromagnetic thrust, parameter perturbation, and external disturbance, this embodiment of the invention also constructs mechanical motion equations: ; In the formula, For the acceleration of the moving part; The velocity of the mover; The total mass of the mover; It is the coefficient of viscous friction; They are respectively and Uncertainty in parameter variation; This includes external disturbances, frictional forces, and unmodeled dynamic disturbance forces; When the system parameters of the control system change, or when there are external disturbances and nonlinear frictional forces, the dynamic equation of the system should be: In the formula, , , This can be understood as the overall control law of a servo system.

[0053] To further establish the error dynamic equation, firstly, the position tracking error is defined according to the following formula. : ; For error Taking the derivative, we obtain the function for calculating the first error: ; right Upon further differentiation, the second error calculation function is obtained. ,because It is also related to the disturbance term, so when considering the presence of disturbances... for: ; in, .

[0054] Furthermore, embodiments of the present invention can design the following fractional-order terminal sliding surfaces. : ; In the formula, This is the sliding surface gain coefficient, i.e., the preset error gain coefficient; It is a fractional order; For terminal index; ; This is a fractional integral operator. (Illustrative) Terminal Index It can achieve finite-time convergence, ensuring that the error does not approach zero indefinitely, but rather converges to zero within a fixed time.

[0055] Further designing the sliding surface dynamics allows for differentiation of the sliding surface, leading to a convergence model for the motion error. : ; As can be seen from the above, Substituting into the system dynamic equations, we have: ; Furthermore, in order to solve for the equivalent control law, i.e., the first control law, based on the motion error convergence model... ,make Without considering disturbances, setting the disturbance term to zero yields the equivalent control law. The calculation formula is: ; in, When there is no disturbance The corresponding equivalent q-axis current command can be combined with the compensation of the second control law to ultimately form the q-axis current of the drive motor.

[0056] Therefore, the present invention can model the electromagnetic thrust equation and mechanical motion equation of a permanent magnet linear synchronous motor, thereby integrating the motor's inherent characteristics, error dynamic information, and the convergence advantages of fractional-order terminal sliding mode control to form a motion error convergence model that combines physical adaptability and high-precision control capabilities. The design of the fractional-order terminal sliding surface endows the model with the characteristics of finite-time convergence and historical error accumulation representation, enabling the accurate quantification of the deviation of the mover trajectory and effective control of error convergence based on the constructed motion error convergence model.

[0057] For step S3, in some embodiments, the second error calculation function includes: an eighth variable indicating the desired acceleration, a ninth variable indicating the mover velocity, a tenth variable indicating the current component of the stator current on the q-axis, a first sub-variable, a second sub-variable, a third sub-variable, and a disturbance term; that is: ; The process of constructing the compensation function expression describing the relationship between the compensation coefficient and the convergence degree of the mover motion error when a disturbance exists includes: Obtain a function term that indicates the smooth correlation between the compensation strength and the degree of error deviation; Based on the baseline trajectory function, the second error calculation function, the first error calculation function, the function term, the eleventh variable used to indicate the adaptive compensation parameter, and the twelfth variable used to indicate the compensation intensity adjustment parameter, a compensation function is constructed. The adaptive compensation parameter is used to quantify the maximum compensation requirement of the current disturbance; the adaptive compensation parameter is positively correlated with the degree of deviation of the motion trajectory of the mover from the reference motion trajectory when a disturbance exists.

[0058] Furthermore, in a preferred embodiment, the compensation function includes: ; ; ; in, Let be the function value of the compensation function, and represent the compensation coefficient. For function terms, To compensate for adaptive parameters, To compensate for the intensity adjustment parameters, for The corresponding time first derivative; For adaptive rate, used to adjust The update speed; The preset leakage coefficient is used to prevent... Unlimited growth; This is the preset upper limit value for the adaptive compensation parameters.

[0059] In illustrative terms, in this embodiment of the invention, the construction logic of the compensation function is actually closely integrated with the actual operating characteristics of the motor. When quantifying the relationship between the compensation coefficient and the convergence degree of the mover motion error, it uses the first error calculation function. and the second error calculation function The relationship introduces a system disturbance term when the mover executes its motion trajectory. Therefore, after obtaining the compensation coefficient through the compensation function, the second control law, which indicates the motion state compensation adjustment amount for controlling the convergence of motion error of the mover when there is a disturbance, can be solved based on the compensation coefficient and the saturation function.

[0060] Furthermore, in this embodiment of the invention, the preset function term adopts a convex Class. The function is used to design the adaptive gain, realize the continuous correlation between the compensation strength and the degree of error deviation, and avoid the discrete jump of the compensation strength. The value of the compensation adaptive parameter is positively correlated with the degree of deviation of the mover trajectory from the reference trajectory. If the error deviation is more serious and the disturbance is greater, the value of the parameter is larger, thereby quantifying the maximum compensation requirement under the current working condition and realizing the adaptive logic that the greater the disturbance, the higher the upper limit of the compensation capability. In a preferred embodiment, the compensation intensity adjustment parameter, which is an engineering adjustable item, is used to adapt to the rated performance of different motors and adjust the sensitivity and amplitude of compensation.

[0061] Therefore, the compensation function constructed in this invention can transform disturbance effects and error states into specific compensation coefficients, ensuring that the compensation amount can match the current disturbance requirements and improving the accuracy of trajectory tracking. The compensation function also fully covers complex disturbance types commonly encountered in engineering, such as nonlinear total disturbance force, changes in mover mass, and changes in viscous friction coefficient, enabling the compensation logic to cope with various operating conditions such as time-varying parameters and external interference, significantly improving the robustness of the system.

[0062] For step S4, in some embodiments, the switching control law, i.e., the second control law, is solved according to the following formula: ; in, This is the second control law. It is a saturation function. , This represents the boundary layer thickness.

[0063] Schematic representation of boundary layer thickness It is a sliding surface A continuous range of surrounding intervals (the interval is) Within this interval, the discontinuous switching control in the original sliding mode control will be replaced by a continuous saturation function. Replacement, when the state of the sliding surface In That is, when within the boundary layer, the control law changes from discontinuous switching to continuous linear regulation; when s exceeds... That is, when outside the boundary layer, the control law still maintains the switching characteristics of the original sliding mode control.

[0064] For step S5, in some embodiments, during the process of determining the target control law, the participation form and weight of the first and second control laws can be dynamically adjusted according to the degree of disturbance, so as to ensure high accuracy when there is no disturbance and achieve stable compensation under disturbance.

[0065] Specifically, the composition logic of the target control law varies depending on the presence / degree of disturbance, and can be categorized into the following cases: Under undisturbed operating conditions, only the first control law is involved. When the system has no external disturbances and no time-varying parameters, the sliding surface state... The system remains on the ideal trajectory where the error converges. At this point, the second control law... That is, there is no need for disturbance compensation; Target control law At this point, the target control law relies solely on the first control law to achieve error convergence.

[0066] Under conditions of mild disturbance, the second control law participates continuously with small amplitudes. When the disturbance is small, the sliding surface state... Within the boundary layer, at this point, the second control law... The continuous linear segment of the saturation function, without switching transitions; Target control law This allows for basic control to be superimposed with small-amplitude continuous compensation, which both suppresses disturbances and avoids jitter.

[0067] When there is severe disturbance, the second control law participates in a switching manner. When the disturbance is large, the sliding surface state... Beyond the boundary layer, at this point, the second control law... This restores the switching characteristics of sliding mode control, ensuring rapid disturbance compensation; however, by constraining the amplitude of the saturation function, It will not exceed the rated thrust / current range of the motor; Target control law This means that basic control is superimposed with a relatively large amount of switching compensation to quickly pull the system back to the ideal trajectory.

[0068] Understandably, the first control law It is the fundamental control variable that enables error convergence when there is no disturbance; it is the second control law. It is the compensating control quantity to offset the disturbance, and the target control law is the superposition of the two. In essence, it is the synergistic effect of basic control and disturbance compensation.

[0069] Furthermore, target control law These are control law level instructions that need to be converted into electrical signals (current / voltage commands) that the motor can execute. If the target control law... If it is a thrust command, then the thrust command can be converted into a q-axis current command by combining the electromagnetic thrust equation; if the target control law is a q-axis current command, then it can be directly converted into a q-axis current command. This serves as the final q-axis current command.

[0070] Then, through the motor's current closed-loop controller and inverter, the q-axis current command is converted into a voltage drive signal for the stator winding, which ultimately controls the strength and direction of the stator magnetic field, and thus controls the thrust output of the mover.

[0071] When the voltage drive signal is input to the stator winding of the motor, the electromagnetic thrust generated by the stator drives the mover to move. At the same time, the position / speed sensor of the motor will provide real-time feedback on the actual state of the mover, such as position and speed, for the calculation of error characteristics and control updates at the next moment, thereby realizing closed-loop control and continuously ensuring that the mover moves along the desired trajectory.

[0072] In a preferred embodiment, the present invention can also be designed with the following controller to generate the final q-axis current command: ; The complete controller expression is: .

[0073] In a preferred embodiment, the present invention can also prove the controller described above using a Lyapunov function, enabling the system to converge to the sliding surface neighborhood in a finite time, thus facilitating stability analysis for sliding mode control and adaptive disturbance compensation systems. First, the Lyapunov function is chosen as V, where V is an auxiliary positive definite function for analyzing system stability: ; In the formula, Then, differentiating with respect to V, we get: ; Substituting the sliding surface, we can obtain: ; in, Assuming the total disturbance, substituting it into the adaptive law yields the following: ; Further analysis revealed: ; because , and when converged to At that time, there are positive numbers. Then we have: ; in, A positive constant, when converged to When K(s) is lower bounded, the following applies. Therefore, through the function construction and control quantity generation of this invention, the motor system can converge to the sliding surface neighborhood in a finite time, and the tracking error is bounded.

[0074] In a preferred embodiment, such as Figure 2 As shown, the adaptive control method for permanent magnet linear synchronous motors of the present invention is applied to the hardware module of a permanent magnet linear synchronous motor control device system of a high-end CNC machine tool, and is realized through the coordinated cooperation of software algorithms and hardware circuits.

[0075] like Figure 2 As shown, the permanent magnet linear synchronous motor control device system includes: a rectifier and filter circuit, an IPM inverter circuit, a permanent magnet linear synchronous motor body, a detection circuit module, a DSP module, an IPM isolation protection drive circuit, and a host computer module. The input side of the rectifier and filter circuit is connected to a three-phase AC power supply to receive three-phase AC power and perform rectification and filtering. Its output side is connected to the DC bus terminal of the IPM inverter circuit to provide a stable DC voltage. The DC input terminal of the IPM inverter circuit is connected to the output terminal of the rectifier filter circuit, and its three-phase AC output terminal is connected to the stator three-phase winding of the permanent magnet linear synchronous motor. It is used to invert DC power into three-phase AC power with adjustable frequency and amplitude according to the control signal, thereby driving the permanent magnet linear synchronous motor mover to perform linear motion. The detection circuit module includes an independent current detection unit and a position and velocity detection unit; The current detection unit uses a Hall current sensor to collect the phase current signal flowing through the output side of the IPM inverter circuit in real time. This current corresponds to the mover current of the permanent magnet linear synchronous motor. After signal conditioning and analog-to-digital conversion, it forms a digital current signal and sends it to the DSP control module. The position and speed detection unit uses a grating ruler as a sensing element to detect the linear displacement and speed of the permanent magnet linear synchronous motor mover in real time, and outputs orthogonal pulse signals and zero-position signals. After signal processing, these signals are converted into digital position and speed information and transmitted to the DSP control module. The DSP control module is based on a digital signal processor and equipped with peripheral auxiliary circuits including a level conversion circuit, a fault acquisition circuit, a crystal clock circuit, a JTAG debugging interface, and a reset circuit. The digital signal processor is responsible for receiving the current, position, and speed digital signals uploaded by the detection circuit module and executing an adaptive fractional-order terminal sliding mode control algorithm: by calculating the error between the given position signal and the actual position signal fed back by the grating ruler, and fusing current and speed information, it generates current control commands in real time, and then modulates a six-channel PWM pulse sequence. Schematic, in this embodiment of the invention, the DSP control module executes the above-mentioned adaptive control method for a permanent magnet linear synchronous motor. After calculating the first control law and the second control law, the DSP control module determines the target control law based on the first and second control laws, and then generates a drive signal based on the target control law to control the motion state of the mover at the next moment.

[0076] The IPM isolation protection drive circuit is arranged between the DSP control module and the IPM inverter circuit. Its input terminal receives the PWM logic signal output by the DSP control module. After opto-isolation and level amplification, it outputs the gate signal that can directly drive the IGBT inside the IPM. It also has the function of real-time detection of overcurrent, overvoltage and overheating faults and execution of hardware protection. The host computer monitoring module establishes a bidirectional data connection with the DSP control module through a serial communication interface. It is used to download control programs to the DSP, adjust control parameters in real time, monitor the system operating status, and collect operating data. The control program is written in C language in the Code Composer Studio integrated development environment. Its control kernel is the adaptive fractional-order terminal sliding mode control algorithm. After compilation, the program is downloaded to the DSP's on-chip memory through the SCI serial port for execution.

[0077] In summary, the permanent magnet linear synchronous motor control device system of this invention is a hardware carrier of the adaptive control method. Based on the coordinated execution of the hardware functions and software algorithms of each module, it ultimately achieves the high-precision and disturbance-resistant control effect of the permanent magnet linear synchronous motor required by high-end CNC machine tools.

[0078] Please see Figure 3 This application also provides an adaptive control device for a permanent magnet linear synchronous motor, which can implement the above-described adaptive control method for a permanent magnet linear synchronous motor. The device includes: The data acquisition module 601 is used to acquire the motor dynamic characteristic data of the permanent magnet linear synchronous motor and the motion characteristic data of the mover at the current moment, and generate an error characteristic quantity to indicate the change law of position error based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment. The first control law generation module 602 is used to substitute the error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data into the motion error convergence model to solve for the first control law; wherein, the motion error convergence model is used to indicate the correlation between the motor's dynamic characteristics, the reference trajectory function of the mover, the motion state of the mover, and the motion trajectory control quantity of the mover; the first control law is used to indicate the basic adjustment amount of the motion state for controlling the motion error convergence of the mover when there is no disturbance; The compensation coefficient generation module 603 is used to generate compensation coefficients based on the error characteristic quantity and the compensation function; wherein, the compensation coefficient is used to quantify the motion state compensation intensity required by the mover to offset the disturbance; the compensation function is used to describe the relationship between the compensation coefficient and the convergence degree of the mover motion error when there is a disturbance; The second control law generation module 604 is used to solve for the second control law based on the compensation coefficient and the saturation function used to suppress the degree of system chattering during the motion trajectory control process of the mover; wherein, the second control law is used to indicate the motion state compensation adjustment amount for controlling the convergence of the motion error of the mover when there is a disturbance. The control module 605 is used to determine a target control law based on the first control law and the second control law, and then generate a drive signal based on the target control law to control the motion state of the mover at the next moment.

[0079] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0080] It should be noted that the device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0081] Those skilled in the art will clearly understand that, for convenience and simplicity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0082] This application also 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 aforementioned adaptive control method for a permanent magnet linear synchronous motor. This electronic device can include any smart terminal such as a tablet computer or an in-vehicle computer.

[0083] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0084] Please see Figure 4 , Figure 4 This illustrates the hardware structure of an electronic device according to another embodiment, the electronic device comprising: The processor 701 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. The memory 702 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this application are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701. The input / output interface 703 is used to implement information input and output; The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704); The processor 701, memory 702, input / output interface 703 and communication interface 704 are connected to each other within the device via bus 705.

[0085] The processor 701 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. This processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0086] The memory 702 can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device or other volatile solid-state storage device.

[0087] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned adaptive control method for a permanent magnet linear synchronous motor.

[0088] It is understood that the content of the above method embodiments is applicable to the present computer storage medium embodiments. The specific functions implemented by the present computer storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0089] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described adaptive control method for a permanent magnet linear synchronous motor.

[0090] It is understood that the content of the above method embodiments is applicable to the embodiments of this computer program product. The specific functions implemented by the embodiments of this computer program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0091] Those skilled in the art will understand that all or some of the steps, apparatuses, or functional modules / units in the methods disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0092] 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. An adaptive control method for a permanent magnet linear synchronous motor, characterized in that, The method includes: Acquire the motor dynamic characteristic data of the permanent magnet linear synchronous motor and the motion characteristic data of the mover at the current moment, and generate an error characteristic quantity to indicate the change law of position error based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment. The error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data are substituted into the motion error convergence model to solve for the first control law; wherein, the motion error convergence model is used to indicate the correlation between the motor's dynamic characteristics, the reference trajectory function of the mover, the motion state of the mover, and the motion trajectory control quantity of the mover; the first control law is used to indicate the basic adjustment amount of the motion state that controls the convergence of the motion error of the mover when there is no disturbance. Based on the error characteristics and the compensation function, compensation coefficients are generated; wherein, the compensation coefficients are used to quantify the motion state compensation intensity required by the mover to counteract the disturbance; the compensation function is used to describe the relationship between the compensation coefficients and the convergence degree of the mover's motion error when a disturbance exists; The second control law is solved based on the compensation coefficient and the saturation function used to suppress the degree of system chattering during the motion trajectory control process of the mover; wherein, the second control law is used to indicate the motion state compensation adjustment amount for controlling the convergence of the motion error of the mover when there is a disturbance. Based on the first control law and the second control law, a target control law is determined, and then a driving signal is generated according to the target control law to control the motion state of the mover at the next moment.

2. The adaptive control method for a permanent magnet linear synchronous motor according to claim 1, characterized in that, The error characteristic quantity includes: a first error characteristic quantity and a second error characteristic quantity; The step of generating an error feature quantity to indicate the pattern of position error change based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment includes: Based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment, a position tracking error is generated; The time dimension derivative of the position tracking error is calculated to generate a first error feature quantity that characterizes the direction of error change as the position error changes over time. The first error feature is differentiated in the time dimension to generate a second error feature that characterizes the error change trend of position error over time.

3. The adaptive control method for a permanent magnet linear synchronous motor according to claim 2, characterized in that, The process of constructing the motion error convergence model includes: Obtain the first error calculation function to calculate the direction of position error change over time; Based on the terminal sign function corresponding to the first error calculation function and the fractional integral operator, a fractional integral term is constructed; wherein, the fractional integral term is used to characterize the historical cumulative information of the position error; Based on the fractional integral term and the first error calculation function, a reference trajectory function is constructed; wherein, the reference trajectory function is used to indicate the reference motion trajectory when the motion error of the mover converges to zero. The fractional integral term in the reference trajectory function is differentiated to generate a fractional differential term; wherein the fractional differential term is used to characterize the variation characteristics of the position error. The first error calculation function in the reference trajectory function is differentiated to generate the second error calculation function; wherein, the second error calculation function is used to calculate the error change trend of position error over time; Based on the relationship between the second error calculation function, the first variable indicating the motor dynamic characteristics, the second variable indicating the mover motion characteristics, and the third variable indicating the motion state adjustment amount, as well as the fractional differential term and the disturbance term, a motion error convergence model is constructed. The fractional derivative term corresponds to an error gain coefficient, which is used to quantify the degree of influence of the fractional derivative term on the motion of the mover to achieve the reference motion trajectory. The disturbance term includes: a fourth variable indicating the change in the total mass of the mover, a fifth variable indicating the change in the coefficient of viscous friction, a sixth variable indicating the total disturbance force of the nonlinear disturbance, and a seventh variable indicating the electromagnetic thrust coefficient. The first variable includes: a first sub-variable indicating the electromagnetic thrust coefficient, a second sub-variable indicating the total mass of the mover, and a third sub-variable indicating the viscous friction coefficient; the second variable includes: a fourth sub-variable indicating the mover acceleration and a fifth sub-variable indicating the mover velocity.

4. The adaptive control method for a permanent magnet linear synchronous motor according to claim 3, characterized in that, The motor dynamic characteristic data includes: electromagnetic thrust coefficient, total mass of the mover, and viscous friction coefficient; the motion characteristic data includes: mover acceleration and mover velocity. The step of substituting the error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data into the motion error convergence model to solve for the first control law includes: The error characteristic quantity is used as the function value of the first error calculation function. The electromagnetic thrust coefficient, the total mass of the mover, and the viscous friction coefficient are used as the values ​​of the first, second, and third sub-variables, respectively. The mover acceleration and the mover velocity are used as the values ​​of the fourth and fifth sub-variables, respectively. The value of the disturbance term is set to zero, and a convergence model of the motion error to be solved is constructed. Under the preset motion error convergence constraint, the third variable used to indicate the motion state adjustment amount in the motion error convergence model to be solved is solved, and the value corresponding to the third variable is used as the first control law; wherein, the motion error convergence constraint is used to indicate that the function value of the motion error convergence model to be solved is zero.

5. The adaptive control method for a permanent magnet linear synchronous motor according to claim 4, characterized in that, The second error calculation function includes: an eighth variable for indicating the desired acceleration, a ninth variable for indicating the mover velocity, a tenth variable for indicating the current component of the stator current on the q-axis, a first sub-variable, a second sub-variable, a third sub-variable, and a disturbance term; The process of constructing the compensation function includes: Obtain a function term that indicates a smooth correlation between the compensation strength and the degree of error deviation; Based on the baseline trajectory function, the second error calculation function, the first error calculation function, the function term, the eleventh variable used to indicate the adaptive compensation parameter, and the twelfth variable used to indicate the compensation intensity adjustment parameter, a compensation function is constructed. The adaptive compensation parameter is used to quantify the maximum compensation requirement of the current disturbance; the adaptive compensation parameter is positively correlated with the degree of deviation of the motion trajectory of the mover from the reference motion trajectory when a disturbance exists.

6. The adaptive control method for a permanent magnet linear synchronous motor according to claim 5, characterized in that, The first error calculation function includes: ； in, This represents the function value of the first error calculation function. For velocity error, it is used to indicate the direction of position error change over time; Indicates the expected velocity of the mover. This indicates the actual velocity of the mover; The second error calculation function includes: ； ； in, This represents the function value of the function expression for calculating the second error. This refers to acceleration error, used to indicate the trend of position error change over time. Indicates the expected acceleration. Indicates actual acceleration. The coefficient of viscous friction, For the total mass of the mover, This is the electromagnetic thrust coefficient. This represents the current component of the stator current on the q-axis. For the disturbance term, This represents the change in the total mass of the mover. This is the change in the coefficient of viscous friction. The total disturbance force of the nonlinear disturbance; The reference trajectory function includes: ； in, Let be the function value of the reference trajectory function, representing the reference trajectory when the motion error of the mover converges to zero. Describes a fractional integral operator. The first error calculation function is expressed as follows. The corresponding terminal symbol function, Represents a fractional integral term. These are the weighting coefficients corresponding to the fractional integral terms. Used to quantify fractional integral terms The extent of the impact; , It is a fractional order; , The exponent of the terminal symbol function; The motion error convergence model includes: ； ； in, for The corresponding first derivative, The function value of the motion error convergence model is used to represent the degree to which the motion trajectory of the mover deviates from the reference motion trajectory; This represents the fractional differential term, used to characterize the variation of position error. The coefficient of viscous friction, For the total mass of the mover, This is the electromagnetic thrust coefficient. This is the first control law.

7. The adaptive control method for a permanent magnet linear synchronous motor according to claim 6, characterized in that, The compensation function includes: ； ； ； in, Let be the function value of the compensation function, and represent the compensation coefficient. For function terms, To compensate for adaptive parameters, To compensate for the intensity adjustment parameters, for The corresponding time first derivative; For adaptive rate, used to adjust The update speed; The preset leakage coefficient is used to prevent... Unlimited growth; This is the preset upper limit value for the adaptive compensation parameters.

8. An adaptive control device for a permanent magnet linear synchronous motor, characterized in that, The device includes: The data acquisition module is used to acquire the motor dynamic characteristic data of the permanent magnet linear synchronous motor and the motion characteristic data of the mover at the current moment, and generate error characteristic quantities to indicate the change law of position error based on the actual position of the mover at the current moment and the preset expected position of the mover at the current moment. The first control law generation module is used to substitute the error characteristic quantity, the motor dynamic characteristic data, and the motion characteristic data into the motion error convergence model to solve for the first control law; wherein, the motion error convergence model is used to indicate the correlation between the motor's dynamic characteristics, the reference trajectory function of the mover, the motion state of the mover, and the motion trajectory control quantity of the mover; the first control law is used to indicate the basic adjustment amount of the motion state that controls the convergence of the motion error of the mover when there is no disturbance. The compensation coefficient generation module is used to generate compensation coefficients based on the error characteristic quantity and the compensation function; wherein, the compensation coefficient is used to quantify the motion state compensation intensity required by the mover to offset the disturbance; the compensation function is used to describe the relationship between the compensation coefficient and the convergence degree of the mover motion error when a disturbance exists; The second control law generation module is used to solve for the second control law based on the compensation coefficient and the saturation function used to suppress the degree of system chattering during the motion trajectory control process of the mover; wherein, the second control law is used to indicate the motion state compensation adjustment amount for controlling the convergence of the motion error of the mover when there is a disturbance. The control module is used to determine the target control law based on the first control law and the second control law, and then generate a drive signal based on the target control law to control the motion state of the mover at the next moment.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the adaptive control method for a permanent magnet linear synchronous motor as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements an adaptive control method for a permanent magnet linear synchronous motor according to any one of claims 1 to 7.

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