Dual-pmsm compound error weight adaptive predictive position synchronization control method

Abstract

The application discloses a double PMSM compound error weight adaptive prediction position synchronous control method, comprising the following steps: S1, according to the mathematical modeling of the double permanent magnet synchronous motor system, an incremental continuous control set model prediction control model is established, and a multi-step prediction is carried out to obtain an output matrix; S2, based on the multi-step prediction output matrix, average tracking error and semi-modal synchronous error are constructed, and a value function is established; S3, according to the design of a Lyapunov function, a weight adaptive update law is designed by using a gradient descent method; S4, an event triggering mechanism is designed for the weight adaptive update law, if an event triggering condition is met, the weight adaptive update is carried out, otherwise, the weight of the last moment is used; S5, the optimal control variable increment is obtained by minimizing through the weight calculation after the update, whether the constraint condition is met is judged, if the constraint condition is met, the optimal control variable increment is directly output, otherwise, the optimal control variable increment is output after being subjected to geometric constraint.

Description

Technical Field

[0001] This invention relates to the field of multi-permanent magnet synchronous motor system technology, and in particular to a dual PMSM composite error weight adaptive predictive position synchronization control method. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in electric vehicles, servo systems, and robotics due to their high power density, high efficiency, high torque-to-inertia ratio, and excellent position control performance. For multi-motor systems, the tracking and synchronization performance of the motors is crucial for system safety and operational efficiency. Therefore, improving the synchronization performance and tracking accuracy of dual PMSM drive systems is of great significance. Model predictive control (MPC), as an advanced algorithm, achieves a good balance between dynamic performance, steady-state accuracy, multivariable constraints, and computational complexity. When combined with synchronous control structures, it can achieve excellent synchronization performance and is widely used in multi-motor synchronous control systems.

[0003] Model predictive control achieves multi-objective optimization through a value function. Weights, as the core parameters of the value function, directly determine the priority of each control objective and are crucial for balancing tracking accuracy, synchronization accuracy, and constraint satisfaction. However, most existing weight tuning methods ultimately employ fixed weight configurations. In actual operation, dual-motor position cooperative control systems often face complex disturbances such as sudden load changes, parameter perturbations, and switching between multiple operating conditions. This causes the optimal weight values ​​to exhibit significant time-varying characteristics. Traditional fixed-weight configuration schemes struggle to dynamically adapt to these time-varying characteristics, easily leading to system performance degradation when deviating from the design conditions.

[0004] Therefore, it is necessary to develop a control method for dual permanent magnet synchronous motor systems with adaptive design weights to solve the problem that fixed-weight dual permanent magnet synchronous motor control systems are difficult to adapt to various working conditions and complex disturbances in practice, and to improve the synchronization performance, control accuracy and robustness of dual permanent magnet synchronous motor drive systems. Summary of the Invention

[0005] The purpose of this invention is to address the problem that the fixed-weight dual permanent magnet synchronous motor control system in the prior art is difficult to adapt to various working conditions and complex disturbances, and to provide a dual PMSM composite error weight adaptive predictive position synchronization control method to improve the synchronization performance, control accuracy and robustness of the dual permanent magnet synchronous motor drive system.

[0006] The technical solution adopted to achieve the purpose of this invention is:

[0007] The dual PMSM composite error weighted adaptive predictive position synchronization control method includes the following steps:

[0008] Step S1: Establish an extended predictive control model for the dual permanent magnet synchronous motor based on the mathematical model of the permanent magnet synchronous motor, and perform multi-step prediction to obtain the output matrix.

[0009] Step S2: Construct the position tracking error of motor x based on the multi-step prediction output matrix in step S1. Position tracking error of the Y motor and synchronization error ,Will , and Reconstructed as average tracking error and semi-modal synchronization error Two orthogonal modes are used, and a value function is established;

[0010] Step S3: Construct the Lyapunov function based on the value function in step S2; design the adaptive weight update law using the gradient descent method based on the Lyapunov function; and prove the stability of the system by calculating the Lyapunov function increment.

[0011] Step S4: For the weight adaptive update law in step S3, an event triggering mechanism is introduced. If the event triggering condition is met, the weight adaptive update is performed; otherwise, the weight of the previous time step is used.

[0012] Step S5: Calculate the value function using the updated weights from step S4. Obtain the optimal control variable increment by minimizing the value function. Determine whether the optimal control variable increment satisfies the constraints. If it does, directly output the effect applied to the dual PMSMs. If it does not, output the effect applied to the dual PMSMs after geometric constraints.

[0013] In the above technical solution, the mathematical model of the permanent magnet synchronous motor is the motion equation of the permanent magnet synchronous motor. The motion equation of the permanent magnet synchronous motor is discretized to obtain the predictive control model of the model prediction. The predictive control model is extended to a state. A dual-motor system model is constructed based on the extended predictive control model. Multi-step prediction is performed on the dual-motor system model to obtain the multi-step prediction output matrix.

[0014] The multi-step prediction output matrix of the dual-motor system model is represented as follows: ; In the formula, This is the output matrix of the multi-step prediction for the dual-motor system model. The state variable matrix of the dual-motor system model. This is the control variable increment matrix for a dual-motor system model. The coefficients of the state variable matrix for multi-step prediction of a dual-motor system model. The coefficients of the control variable increment matrix for multi-step prediction of a dual-motor system model; in, and The calculation formula is: ; In the formula, for The output matrix of the dual-motor system model at any given time. for The output matrix of the dual-motor system model at any given time. for The output matrix of the dual-motor system model at any given time. For the prediction domain, for The control variable increment matrix of the dual-motor system model at any given time, and ,in, This represents the increment of the control variable for motor x in the dual-motor system model. This represents the increment of the control variable for motor y in the dual-motor system model. for k The control variable increment matrix of the dual-motor system model at time +1 for The control variable increment matrix of the dual-motor system model at any given time. This is the control domain.

[0015] In the above technical solution, in step S2, the formula for calculating the value function is: ; In the formula, For value function, The amount of change in the prediction domain over time. for The average tracking error at time t, As the average tracking error weight, For the weight of the semi-modal synchronization error, for The half-modal synchronization error at time t, To control the incremental weights of variables, for Increment of control variables at time intervals For the current moment, The time-varying amount of the control domain; The calculation formula is: ; The calculation formula is: ; The calculation formula is: ; In the formula, Given the position of motor x, The actual position measured for motor x; The calculation formula is: ; In the formula, Given the position of the Y motor, The actual position measured for the Y motor; Synchronization error The calculation formula is: .

[0016] In the above technical solution, in step S3, the Lyapunov function is represented as: ; In the formula, for k The average tracking error at time t, for k The half-modal synchronization error at time t, for k Weight of time-averaged tracking error Compared with its ideal value The deviation, and , for k Weight of time-mode synchronization error Compared with its ideal value The deviation, and , For adaptive rate;

[0017] The adaptive weight update law is as follows: ; In the formula, for k The average tracking error weight at time +1 for k Weight of the half-modal synchronization error at time +1 Adjust the gain to weight the average tracking error. Adjust the gain for the weight of the half-modal synchronization error.

[0018] In the above technical solution, the formula for calculating the Lyapunov function increment in step S3 is: ; In the formula, for The average tracking error at time t, for The half-modal synchronization error at time t, for The deviation of the average tracking error weight from its ideal value at any given time. for The deviation of the half-modal synchronization error weight from its ideal value at time t. for Weight of time-averaged tracking error Compared with its ideal value deviation, for Weight of time-mode synchronization error Compared with its ideal value deviation, for The average tracking error at time t, for The half-modal synchronization error at time t; definition The total error matrix is... Take the maximum error ,in, For any small positive number, For closed-loop shrinkage rate, when hour, The system is stable; when At that time, sequence Finally entering compaction The system is stable and compact. ,in, This is an upper bound constant for the absolute value of the weight estimation error.

[0019] In the above technical solution, step S4, the event triggering mechanism includes the following steps:

[0020] Step a, Initialization: Select the average tracking error trigger threshold. and half-modal synchronization error trigger threshold Initial trigger time k 0=0, the initial average tracking error weight Weights of semi-modal synchronization error ;

[0021] Step b, at each sampling time k Acquire online data and perform calculations and and transform into and ,like and ,in, It is the square of the average tracking error at the previous moment. If the sum of the squares of the semi-modal synchronization error at the previous moment is equal to the event trigger condition, then the weight update is performed, and the update is updated. for ,in, If the previous sampling time is used, otherwise, keep the weights unchanged, use the current weights to solve the MPC, output the increment of the control variable, and simultaneously select... ,in, As the trigger threshold, and ;

[0022] Step c, repeat steps a and b.

[0023] In the above technical solution, the geometric constraint condition for the optimal control variable increment in step S5 is expressed as follows: ; ; In the formula, for The minimum value of the control variable increment limit at any given time. for Increment of control variables at time intervals for The maximum value of the control variable increment limit at any given time. To control the maximum limit value of the variable, for Control variables at time, As the base velocity, The maximum value of the control variable below the base speed, and ,in, This is the maximum current value. The mechanical angular velocity of the motor. This represents the maximum value of the control variable above the base speed. This is the symbol for a dual permanent magnet synchronous motor. , This refers to motor x in a dual permanent magnet synchronous motor system. This refers to the Y-motor in a dual permanent magnet synchronous motor. The calculation formula is: ; In the formula, This is the maximum voltage value. It is the q-axis inductance. To control variables, For magnetic linkage, It is the d-axis inductance.

[0024] A second aspect of the present invention is an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the dual PMSM composite error weight adaptive predictive position synchronization control method.

[0025] A third aspect of the present invention is a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the dual PMSM composite error weight adaptive predictive position synchronization control method.

[0026] A fourth aspect of the present invention is a computer program product comprising computer-executable instructions, which, when executed, are used to implement the dual PMSM composite error weight adaptive predictive position synchronization control method.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] 1. The error reconstruction mechanism based on orthogonal transformation proposed in this invention can achieve complete decoupling of multi-objective optimization. Through linear transformation, the tracking error and synchronization error that are coupled in the value function are naturally decoupled into two orthogonal independent modes: "average tracking error" and "semi-modal synchronization error". This reduces the number of weights to be tuned and clarifies the physical meaning of the error term in the value function.

[0029] 2. This invention designs a gradient descent-based adaptive weight update law and provides a rigorous stability proof, enabling the system to adapt to multiple operating conditions. Simultaneously, based on Lyapunov stability theory, it rigorously proves the eventual uniformity and boundedness of the closed-loop system, providing a robust safety guarantee while ensuring high-precision position synchronization.

[0030] 3. This invention introduces an event-triggered mechanism to achieve an optimal balance between computational efficiency and control precision. The system only activates dynamic optimization and updating of weights when the operating error exceeds a preset threshold, effectively reducing redundant calculations in steady state and enhancing the engineering application value of the algorithm. Attached Figure Description

[0031] Figure 1 The diagram shown is a structural block diagram of the dual PMSM composite error weight adaptive prediction position synchronization control method of the present invention.

[0032] Figure 2 The diagram shown is a flowchart of the dual PMSM composite error weight adaptive prediction position synchronization control method of the present invention. Detailed Implementation

[0033] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0034] Example 1

[0035] The dual PMSM composite error weighted adaptive predictive position synchronization control method, refer to Figure 1 , Figure 2 This includes the following steps:

[0036] Step S1: Based on the mathematical model of the permanent magnet synchronous motor, establish an incremental predictive control model for the dual permanent magnet synchronous motor, and perform multi-step prediction to obtain the output matrix.

[0037] Furthermore, the mathematical model of the permanent magnet synchronous motor is the motion equation of the permanent magnet synchronous motor, expressed as: ; In the formula, The moment of inertia of the motor. The torque coefficient of the PMSM is given, and , For extreme logarithms, For magnetic linkage, For q-axis current, For load torque, The coefficient of viscous friction, For mechanical angular velocity, This refers to the rotor angular position. For time, This is the symbol for a dual permanent magnet synchronous motor. , This refers to motor x in a dual permanent magnet synchronous motor system. It is the Y motor in a dual permanent magnet synchronous motor.

[0038] Let the state variable Output variables By discretizing the motion equations of the permanent magnet synchronous motor, a predictive control model is obtained, which is expressed as: ;

[0039] In the formula, for k The state variable at time +1 for k The state variable at time t, For state variable coefficients, To control the coefficients of the variables, for k Control variables at time, For the disturbance term, For output variables, To output the coefficients of the variables, where, .

[0040] in, The calculation formula is: ; In the formula, To control the cycle.

[0041] The calculation formula is: ; The calculation formula is: ;

[0042] Define extended state The extended predictive control model is then: ; In the formula, for k Extended state variables at time +1 for k Extended state variables at time t, To control the increment of variables, To expand the coefficients of the state variables, To extend the control variable increment coefficients in the model, To expand the output variables under the model, For the output variable coefficients under the extended model, where, .

[0043] in, The calculation formula is: ; In the formula, It is the identity matrix. .

[0044] The calculation formula is: ; In the formula, .

[0045] The dual-motor system model is constructed based on the extended predictive control model and is represented as follows: ; In the formula, for k The state variable matrix of the dual-motor system model at time +1 fork The state variable matrix of the dual-motor system model at any given time. for k The control variable increment matrix of the dual-motor system model at any given time. for k The output matrix of the dual-motor system model at any given time. The coefficients of the state variable matrix for the dual-motor system model. For the control variable increment matrix coefficients of the dual-motor system model, These are the output matrix coefficients of the dual-motor system model.

[0046] in, The calculation formula is: ; In the formula, and These are the extended state variables for motor x and motor y, respectively.

[0047] The calculation formula is: ; In the formula, and These are the output variables under the extended models of the x-motor and y-motor, respectively.

[0048] The calculation formula is: ; In the formula, for The increment of the control variable for motor x in the dual-motor system model at any given time. for The increment of the control variable of motor y in the dual-motor system model at any given time.

[0049] The calculation formula is: ; In the formula, and These are the extended state variable coefficients for motor x and motor y, respectively.

[0050] The calculation formula is: ; In the formula, and These are the control variable increment coefficients under the extended models of motor x and motor y, respectively.

[0051] The calculation formula is: ; In the formula, and These are the output variable coefficients under the extended models of the x-motor and y-motor, respectively.

[0052] Let the control domain be The prediction domain is ( ),have: ; In the formula, This is the output matrix of the multi-step prediction for the dual-motor system model. for k The output matrix of the dual-motor system model at time +1 for k The output matrix of the dual-motor system model at time +2. for The output matrix of the dual-motor system model at any given time. This is the control variable increment matrix for a dual-motor system model. for k The control variable increment matrix of the dual-motor system model at time +1 for The control variable increment matrix of the dual-motor system model at any given time.

[0053] The formula for calculating the multi-step prediction output matrix is ​​as follows: ; In the formula, This is the multi-step prediction output matrix for a dual-motor system model. The state variable matrix of the dual-motor system model. The coefficients of the state variable matrix for multi-step prediction of a dual-motor system model. The coefficients of the control variable increment matrix for multi-step prediction of a dual-motor system model.

[0054] in, The calculation formula is: ; The calculation formula is: ; Step S2: Construct the position tracking error of the x motor and y motor based on the multi-step prediction output matrix obtained in step S1. , Synchronization error ,Will , and Reconstructing the average tracking error as the overall position tracking accuracy of the reaction system The semi-modal synchronization error of the synchronization control accuracy of the reaction system Two orthogonal modes, and establish a system containing and The value function, in which the weights corresponding to the average tracking error and the semi-modal synchronization error are defined as follows: and .

[0055] Furthermore, the position tracking error between the two PMSMs is defined. , and synchronization error for: ; In the formula, Given the position of motor x, The actual position measured for motor x. Given the position of the Y motor, The actual position measured for the Y motor.

[0056] The original value function in traditional predictive position synchronization control is: ; In the formula, For value function, for The square of the tracking error of the motor at time x. for The square of the tracking error of the motor at time y. for The square of the synchronization error of the two motors at any given time. for Increment of control variables at time intervals k For the current moment, j , τ These represent the time-varying values ​​in the prediction and control domains, respectively. The tracking error weight for motor x. The tracking error weights for the y motor are... For synchronization error weights, To control the incremental weights of variables, Obtained through routine experiments or empirical tuning.

[0057] The calculation formula is: ; The calculation formula is: ; Therefore In the coordinate system, the original value function in traditional predictive position synchronization control becomes: ; If let Then the cross term disappears, and the value function simplifies to: ; definition Then the value function becomes a completely decoupled form: ; In the formula, As the average tracking error weight, This represents the weighting of the semi-modal synchronization error.

[0058] At this point, the tracking error of the dual-motor system is decomposed into two orthogonal modes: the average tracking error and the synchronization error. Lyapunov adaptive laws are designed to adjust the weights of the average tracking error. and half-modal synchronization error weights .

[0059] Step S3, based on the value function in step S2 , , and A Lyapunov function is constructed, and a weight adaptive update law is designed based on the gradient descent method according to the Lyapunov function. The weight adaptive update law is used to adjust the weights in the value function in step S2 online, and the stability of the system is proved by calculating the Lyapunov function increment.

[0060] Furthermore, Lyapunov functions are designed as follows: ; In the formula, for k The average tracking error at time t, for k The half-modal synchronization error at time t, for k Weight of time-averaged tracking error Compared with its ideal value The deviation, and , for k Weight of time-mode synchronization error Compared with its ideal value The deviation, and , The adaptive rate is used to adjust the convergence speed of the weights, and its value range is (0, 1).

[0061] Assume that the effect of weight changes on the error is approximately a negative gradient relationship: ; In the formula, , Adjust the gain for weights, increase , This will make MPC more actively reduce and .

[0062] Through the Taking the partial derivatives, we update the weights using gradient descent. The adaptive weight update law is: ; ; In the formula, for The deviation of the average tracking error weight from its ideal value at any given time. for The deviation of the half-modal synchronization error weight from its ideal value at time t. and These are Lyapunov function pairs. and The partial derivatives of .

[0063] in, and The partial derivatives are calculated as follows: ; ; Will and Substituting the formula for calculating partial derivatives into the formula for calculating the adaptive update law of weights, we get: ; Assuming ideal average tracking error weights Synchronization error with ideal half-mode If is a constant, then: ; The practical weight adaptive update law is: ; In adaptive control theory, and It is the theoretical reference value used in Lyapunov stability analysis to prove the convergence of the adaptive weight update law. According to the calculation formula of the adaptive weight update law, when the controller is running in actual operation, it only needs to update the average tracking error weight or the semi-modal synchronization error weight according to the real-time average tracking error or the semi-modal synchronization error, and it can automatically converge from any initial value to the ideal value without knowing the specific value of the ideal weight in advance.

[0064] In engineering, a forgetting factor is often desired. and adaptive rate To maintain synchronization in terms of magnitude, therefore, the derivation that appears... Directly "absorbed" into the new adaptive rate, Re-marked as And assume ideal weights The final adaptive weight update law is obtained as follows: ; In the formula, As an adaptive rate, it can prevent the weights from growing unbounded and pull the weights back to zero.

[0065] Furthermore, Lyapunov function increments The calculation formula is: ; Will Divided into error shrinkage terms and weighted influence items Two parts, including the error shrinkage term. The calculation formula is: ; Weighting Influence Items The calculation formula is: ; for Assume there exists a constant This makes for any time k satisfy: ; In the formula, For the Euclidean norm, This represents the closed-loop shrinkage rate.

[0066] and middle and It only affects the MPC value function, and does not change the value function. The closed-loop contraction rate. In practical applications, when the weights are bounded, MPC can always guarantee... satisfy ,therefore: ; ; In the formula, The total error matrix is... .

[0067] for According to the final adaptive update law for weights, fully expanding the squared terms, we get: ; Substitute it The first part Therefore: ; right Part 2 Perform the same calculation and substitute the values. From the calculation formula, we can obtain: ; Using Young's inequality ,Right now, For any a , b Both are true. (Take) , ,but: ; Similarly, we have: ; Substitute it In the middle, there are: ; Rearranging the above equation, we get: ; Simplifying the above equation by canceling out identical terms, we get: ; Divided into error shrinkage terms and weighted influence items It consists of two parts, therefore: ; Depend on ,have: ; Substitution From this, we can further scale it down to: ; Define a function of the Euclidean norm of the total error. : ; when When it is large, From the positive fourth term leading, Possibly positive; when When smaller, From negative quadratic terms Dominant. Therefore, it exists. R >0, so that when .

[0068] That is when hour, Therefore, substitute From this, we can obtain: ; Specifically, solving the inequalities : ; get: ; Take the maximum error ,in, For any small positive number, then when Sometimes, ,Will Substitution ,have, ; Within the aforementioned area, Strictly true if and only if The equality holds, indicating that within this region, the adjustment effect of the weight adaptive update law will not compromise the stability of the system.

[0069] for ,Although It may make The upper bound is positive, but according to the standard Lyapunov eventually unified boundedness, there exists a constant. Such that for any initial conditions, the sequence Finally entering compaction ,in, This is a constant that serves as the upper bound for the absolute value of the weight estimation error. This depends on the initial conditions and system parameters. This compact set indicates that the tracking error and weight deviation of the closed-loop system are both uniformly bounded. The adaptive weight update law ensures that the system state does not exceed this compact set during this process, i.e., the system is stable.

[0070] Step S4: Design an event triggering mechanism for the weight adaptive update law in step S3. If the event triggering condition is met, perform weight adaptive update and use the updated weight for the calculation of the value function in step S5. Otherwise, use the weight from the previous moment. By designing the event triggering mechanism, reduce the consumption of computing resources and update the weight only when the error is large.

[0071] Furthermore, the event triggering mechanism includes the following steps: Step a, Initialization: Select the average tracking error trigger threshold. and half-modal synchronization error trigger threshold Initial trigger time k 0=0, the initial average tracking error weight Weights of semi-modal synchronization error ; Step b, at each sampling time (i.e., the current time). k Acquire online data and perform calculations and and transform into and ,like or If the event triggering condition is met, the weight update will be performed, and the update will be updated. for ( (If the previous sampling time is used); otherwise, keep the weights unchanged, use the current weights to solve the MPC, and output the increment of the control variable. Simultaneously select... ,in, As the trigger threshold, and ; Step c, repeat steps a and b.

[0072] Because the stability analysis in step S4 satisfies uniform boundedness, the weights are fixed within the trigger interval, and the adaptive weight update law has no effect. However, according to the stability of MPC, the position tracking error... , and the average tracking error derived therefrom and semi-modal synchronization error It still decays exponentially, therefore the Lyapunov function of the system... In the middle, the weighted bias remains unchanged, while the error component decreases exponentially, so V ( k Strictly decreasing until the next trigger.

[0073] Step S5: Utilize the updated average tracking error weights from step S4. and half-modal synchronization error weights Recalculate the value function The optimal control variable increment is obtained by minimizing the value function at this time. It is then determined whether the optimal control variable increment satisfies the constraint conditions. If it does, the output is directly applied to the two PMSMs; otherwise, the output is applied to the two PMSMs after geometric constraints.

[0074] Furthermore, when the motor is running above its base speed, its operating state is simultaneously affected by... and The constraints are as follows. Therefore, the geometric constraints are: The limiting range is expressed as: ; ; In the formula, for The minimum value of the control variable increment limit at any given time. for Increment of control variables at time intervals for The maximum value of the control variable increment limit at any given time. To control the maximum limit value of the variable, for Control variables at time, As the base velocity, The maximum value of the control variable below the base speed, and ,in, This is the maximum current value. The mechanical angular velocity of the motor. The maximum value of the control variable above the base speed; The calculation formula is: ; In the formula, This is the maximum current value. This is the maximum voltage value. for Shaft inductor, To control variables, For magnetic linkage, for Shaft inductance.

[0075] The geometric constraints for the optimal control variable increment are detailed in the patent with publication number CN117955374A. The inventive point of this application is not here and will not be repeated.

[0076] Example 2

[0077] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the dual PMSM composite error weight adaptive predictive position synchronization control method described in Embodiment 1 above.

[0078] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0079] Example 3

[0080] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the dual PMSM composite error weight adaptive predictive position synchronization control method described in Embodiment 1 above.

[0081] The computer-readable storage medium in this embodiment can be an internal storage unit of the terminal, such as the terminal's hard disk or memory; the computer-readable storage medium in this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the terminal; furthermore, the computer-readable storage medium can include both the terminal's internal storage unit and external storage devices.

[0082] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0083] Example 4

[0084] This embodiment provides a computer program product including computer-executable instructions stored on a machine-readable storage medium (such as a disk, flash memory, or optical disk). When executed by at least one data processing device (such as a microprocessor or digital signal processor), the instructions cause the data processing device to implement the steps in the dual PMSM composite error weight adaptive predictive position synchronization control method described in Embodiment 1.

[0085] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A dual PMSM composite error weighted adaptive predictive position synchronization control method, characterized in that, Includes the following steps: Step S1: Establish an extended predictive control model for the dual permanent magnet synchronous motor based on the mathematical model of the permanent magnet synchronous motor, and perform multi-step prediction to obtain the output matrix. Step S2: Construct the position tracking error of motor x based on the multi-step prediction output matrix in step S1. Position tracking error of the Y motor and synchronization error ,Will , and Reconstructed as average tracking error and semi-modal synchronization error Two orthogonal modes are used, and a value function is established; Step S3: Construct the Lyapunov function based on the value function in step S2; design the adaptive weight update law using the gradient descent method based on the Lyapunov function; and prove the stability of the system by calculating the Lyapunov function increment. Step S4: For the weight adaptive update law in step S3, an event triggering mechanism is introduced. If the event triggering condition is met, the weight adaptive update is performed; otherwise, the weight of the previous time step is used. Step S5: Calculate the value function using the updated weights from step S4. Obtain the optimal control variable increment by minimizing the value function. Determine whether the optimal control variable increment satisfies the constraints. If it does, directly output the effect applied to the dual PMSMs. If it does not, output the effect applied to the dual PMSMs after geometric constraints.

2. The dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1, characterized in that, The specific steps of step S1 are as follows: the mathematical model of the permanent magnet synchronous motor is the motion equation of the permanent magnet synchronous motor. The motion equation of the permanent magnet synchronous motor is discretized to obtain the predictive control model of the model prediction. The predictive control model is extended to a state. A dual-motor system model is constructed based on the extended predictive control model. Multi-step prediction is performed on the dual-motor system model to obtain the multi-step prediction output matrix. The multi-step prediction output matrix of the dual-motor system model is represented as follows: ； In the formula, This is the output matrix of the multi-step prediction for the dual-motor system model. The state variable matrix of the dual-motor system model. This is the control variable increment matrix for a dual-motor system model. The coefficients of the state variable matrix for multi-step prediction of a dual-motor system model. The coefficients of the control variable increment matrix for multi-step prediction of a dual-motor system model; in, and The calculation formula is: ； In the formula, for The output matrix of the dual-motor system model at any given time. for The output matrix of the dual-motor system model at any given time. for The output matrix of the dual-motor system model at any given time. For the prediction domain, for The control variable increment matrix of the dual-motor system model at any given time, and ,in, This represents the increment of the control variable for motor x in the dual-motor system model. This represents the increment of the control variable for motor y in the dual-motor system model. for k The control variable increment matrix of the dual-motor system model at time +1 for The control variable increment matrix of the dual-motor system model at any given time. This is the control domain.

3. The dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1, characterized in that, In step S2, the formula for calculating the value function is: ； In the formula, For value function, The amount of change in the prediction domain over time. for The average tracking error at time t, As the average tracking error weight, For the weight of the semi-modal synchronization error, for The half-modal synchronization error at time t, To control the incremental weights of variables, for Increment of control variables at time intervals For the current moment, The time-varying amount of the control domain; The calculation formula is: ； The calculation formula is: ； The calculation formula is: ； In the formula, Given the position of motor x, The actual position measured for motor x; The calculation formula is: ； In the formula, Given the position of the Y motor, The actual position measured for the Y motor; Synchronization error The calculation formula is: 。 4. The dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1, characterized in that, In step S3, the Lyapunov function is represented as: ； In the formula, for k The average tracking error at time t, for k The half-modal synchronization error at time t, for k Weight of time-averaged tracking error Compared with its ideal value The deviation, and , for k Weight of time-mode synchronization error Compared with its ideal value The deviation, and , For adaptive rate; The adaptive weight update law is as follows: ； In the formula, for k The average tracking error weight at time +1 for k Weight of the half-modal synchronization error at time +1 Adjust the gain to weight the average tracking error. Adjust the gain for the weight of the half-modal synchronization error.

5. The dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1, characterized in that, In step S3, the formula for calculating the Lyapunov function increment is: ； In the formula, for The average tracking error at time t, for The half-modal synchronization error at time t, for The deviation of the average tracking error weight from its ideal value at any given time. for The deviation of the half-modal synchronization error weight from its ideal value at time t. for Weight of time-averaged tracking error Compared with its ideal value deviation, for Weight of time-mode synchronization error Compared with its ideal value deviation, for The average tracking error at time t, for The half-modal synchronization error at time t; definition The total error matrix is... Take the maximum error ,in, For any small positive number, For closed-loop shrinkage rate, when hour, The system is stable; when At that time, sequence Finally entering compaction The system is stable and compact. ,in, This is an upper bound constant for the absolute value of the weight estimation error.

6. The dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1, characterized in that, In step S4, the event triggering mechanism includes the following steps: Step a, Initialization: Select the average tracking error trigger threshold. and half-modal synchronization error trigger threshold Initial trigger time k 0=0, the initial average tracking error weight Weights of semi-modal synchronization error ; Step b, at each sampling time k Acquire online data and perform calculations and and transform into and ,like and ,in, It is the square of the average tracking error at the previous moment. If the sum of the squares of the semi-modal synchronization error at the previous moment is equal to the event trigger condition, then the weight update is performed, and the update is updated. for ,in, If the previous sampling time is used, otherwise, keep the weights unchanged, use the current weights to solve the MPC, output the increment of the control variable, and simultaneously select... ,in, As the trigger threshold, and ; Step c, repeat steps a and b.

7. The dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1, characterized in that, In step S5, the geometric constraint condition for the optimal control variable increment is expressed as follows: ； ； In the formula, for The minimum value of the control variable increment limit at any given time. for Increment of control variables at time intervals for The maximum value of the control variable increment limit at any given time. To control the maximum limit value of the variable, for Control variables at time, As the base velocity, The maximum value of the control variable below the base speed, and ,in, This is the maximum current value. The mechanical angular velocity of the motor. This represents the maximum value of the control variable above the base speed. This is the symbol for a dual permanent magnet synchronous motor. , This refers to motor x in a dual permanent magnet synchronous motor system. This refers to the Y-motor in a dual permanent magnet synchronous motor. The calculation formula is: ； In the formula, This is the maximum voltage value. It is the q-axis inductance. To control variables, For magnetic linkage, It is the d-axis inductance.

8. An electronic device, characterized in that, include: One or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1.

9. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed, are used to implement the dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1.

10. A computer program product, characterized in that, The aforementioned computer program product includes computer-executable instructions that, when executed, implement the dual PMSM composite error weight adaptive predictive position synchronization control method as described in claim 1.

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