Permanent magnet synchronous motor model prediction current feedback correction control method and system

Abstract

This invention belongs to the field of model-based predictive current control for permanent magnet synchronous motors (PMSMs), specifically involving a current feedback correction control method and system for PMSMs. It aims to address the problem that existing technologies cannot improve the steady-state current control performance of PMSMs with minimal added program overhead. The invention includes: predicting the predicted control current at time k+1 based on the actual control currents of the PMSM's d-axis and q-axis at the current time k; obtaining the feedback correction value at the current time k based on the actual and predicted control currents of the PMSM's d-axis and q-axis at both times k and k-1; combining the above parameters to obtain the predicted control current of the PMSM at time k+1 after feedback correction, and performing current control on the PMSM. This invention introduces feedback correction into the predictive model and determines the feedback correction coefficients using a particle swarm optimization algorithm, thereby improving the steady-state current control performance of the PMSM.

Description

Technical Field

[0001] This invention belongs to the field of model-predicted current control for permanent magnet synchronous motors, and specifically relates to a current feedback correction control method and system for model-predicted permanent magnet synchronous motors. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) have been widely used in industrial fields such as electric vehicles and servo systems due to their advantages of simple structure and high efficiency. These applications require PMSM drive systems to have good torque control performance, which means that PMSM drive systems should have good current control performance. Over the past decade, many scholars have studied different current control schemes for PMSMs, such as hysteresis control, direct torque control, and model predictive current control. Among these, predictive current control can be designed for multiple control objectives and can be used for multi-input multi-output system control, eliminating the effects of current cross-coupling, thus offering significant advantages over other control methods.

[0003] In model predictive control (MMC), finite set MMC offers advantages such as eliminating the need for modulation modules, making it widely used in motor control. Current research on finite set MMC current predictive control primarily focuses on improving its static characteristics and reducing current ripple by increasing the number of basic voltage vectors and the number of advance prediction steps. However, these methods all increase the computational load of the model, thus limiting the switching frequency.

[0004] Finite set model predictive control typically uses open-loop models. In practical applications, the accuracy of its predictions is affected by various factors, including the nonlinearity and time-varying nature of the controlled object, parameter accuracy, and random disturbances. This uncertainty also changes with motor and load conditions, leading to unstable motor control. Therefore, providing a model predictive current optimization control method for permanent magnet synchronous motors (PMSMs) that improves the steady-state current control performance of PMSMs without adding to the program's execution burden is a technical challenge that urgently needs to be addressed by those skilled in the art. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, namely, the inability of existing technologies to improve the current steady-state control performance of permanent magnet synchronous motors with minimal additional program execution overhead, this invention provides a model-predicted current feedback correction control method for permanent magnet synchronous motors. The control method includes:

[0006] Based on the actual control currents of the permanent magnet synchronous motor on the d-axis and q-axis at the current time k, the predicted control currents of the permanent magnet synchronous motor on the d-axis and q-axis at time k+1 are predicted using the permanent magnet synchronous motor model.

[0007] Based on the actual and predicted control currents of the d-axis and q-axis of the permanent magnet synchronous motor from the initial time to the current time k, the feedback correction value at the current time k is obtained through a feedback correction model.

[0008] Based on the feedback correction value at the current time k and the predicted control currents of the permanent magnet synchronous motor at the d-axis and q-axis at the time k+1, the predicted control currents of the permanent magnet synchronous motor at the time k+1 after feedback correction are obtained.

[0009] Based on the predicted control currents of the permanent magnet synchronous motor at time k+1 after feedback correction, the current control of the permanent magnet synchronous motor at time k+1 is performed.

[0010] In some preferred embodiments, the d-axis and q-axis of the permanent magnet synchronous motor are the two axes of the dq coordinate system of the current feedback correction control of the permanent magnet synchronous motor;

[0011] The dq coordinate system is a synchronous rotating coordinate system obtained by rotating the static coordinate system formed by taking the axes of the three-phase symmetrical windings of the permanent magnet synchronous motor (A, B, and C) as the a, b, and c axes, respectively, with the magnetic flux direction as the d axis and the position 90° electrical angle ahead of the d axis as the q axis.

[0012] In some preferred embodiments, the feedback correction value at the current time k is obtained by:

[0013] ;

[0014] in, and For the current moment k Feedback correction value, and Each represents the current time. k The actual control currents of the d-axis and q-axis of a permanent magnet synchronous motor. and The current moment is obtained by predicting the current moment from the previous moment. k Predictive control currents for the d-axis and q-axis of a permanent magnet synchronous motor. Representatives will proceed with 1~ k Moment Summation, Representatives will proceed with 1~ k Moment Summation, and These are the feedback correction coefficients tuned using a particle swarm optimization method.

[0015] In some preferred embodiments, the feedback correction coefficients tuned using the particle swarm optimization method are tuned as follows:

[0016] Construct a particle swarm containing N particles, and set the 4-dimensional target search space position parameters of each particle to be tuned. and The value;

[0017] Randomly initialize the initial position and initial velocity of each particle, according to the parameters to be tuned. and The characteristics of the first search will be The range is set to [0.5, 2]. , The range is set to [0, 1e-3], the particle flight velocity range is set to [-0.05, 0.05], and the feedback correction value is... and The amplitude limit is set to [-1, 1];

[0018] The particle corresponding to the tuning point and The values ​​are substituted into the Simulink simulation model of the permanent magnet synchronous motor, and the Simulink simulation model of the permanent magnet synchronous motor is run to make the permanent magnet synchronous motor run stably under the actual operating conditions. The particle fitness is constructed as the square of the difference between the reference current values ​​of the d-axis and q-axis and the actual current values ​​when the permanent magnet synchronous motor is running stably.

[0019] The i-th particle is generated by the th t Dai Xiangdi t During the +1 generation evolution, particles with lower fitness are preferred, and their position vectors are updated accordingly.

[0020] The fitness of each particle is optimized iteratively until the set termination condition is met, and the tuned feedback correction coefficient is obtained.

[0021] In some preferred embodiments, the step of setting the 4D target search space position parameter of each particle to be tuned is... and The value is obtained by:

[0022] ;

[0023] in, for In the particle swarm of particles, the first The position of each particle in the 4-dimensional target search space is represented. for In the particle swarm of particles, the first The four position parameters of each particle in the 4-dimensional target search space correspond to the parameters to be tuned. and .

[0024] In some preferred embodiments, the In the particle swarm of particles, the first There are 1 particle whose flight speed is:

[0025] ;

[0026] in, for In the particle swarm of particles, the first The velocity of a particle in a 4-dimensional target search space is represented by... for In the particle swarm of particles, the first Four flight velocity parameters of a particle in a 4-dimensional target search space.

[0027] In some preferred embodiments, the method for constructing the particle fitness as the square of the difference between the reference current values ​​and the actual current values ​​of the d-axis and q-axis of the permanent magnet synchronous motor is as follows:

[0028] ;

[0029] in, For particle fitness, and These represent the d-axis command value and the actual current value during stable operation of the permanent magnet synchronous motor, respectively. and These are the q-axis command value and the actual current value when the permanent magnet synchronous motor is running stably.

[0030] In some preferred embodiments, the method for updating the particle position vector with a lower particle fitness is as follows:

[0031] ;

[0032] in, For the updated number The next iteration In the particle swarm of particles, the first The position vectors of each particle. For the updated number The next iteration In the particle swarm of particles, the first The position vectors of each particle. For the first The next iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space. , representing the four dimensions of the particle's position vector and velocity vector, respectively.

[0033] In some preferred embodiments, the first The next iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space It is represented as:

[0034] ;

[0035] in, For the t-th iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space. The preset inertia coefficient, The flight step size of a particle toward its individual optimal value. The preset self-learning coefficient, This represents the step size of a particle's flight toward the global optimum. The preset global learning coefficients, and A random number in the range (0,1). For the first One particle in front The optimal value of the individual that minimizes the particle's fitness in the next iteration is located at this position. For all particles in front The location of the global optimal solution that minimizes the particle fitness in the next iteration. , representing the four dimensions of the particle's position vector and velocity vector, respectively.

[0036] In another aspect, the present invention proposes a current feedback correction control system for model prediction of a permanent magnet synchronous motor, the control system comprising:

[0037] The current prediction module is configured to predict the d-axis and q-axis predicted control current of the permanent magnet synchronous motor at time k+1 based on the actual control current of the permanent magnet synchronous motor at the current time k and through the permanent magnet synchronous motor model.

[0038] The feedback correction value acquisition module is configured to obtain the feedback correction value at the current time k based on the actual control current and predicted control current of the d-axis and q-axis of the permanent magnet synchronous motor from the initial time to the current time k through the feedback correction model.

[0039] The feedback correction module is configured to obtain the d-axis and q-axis predicted control currents of the permanent magnet synchronous motor at time k+1 after feedback correction based on the feedback correction value at the current time k and the predicted control currents of the permanent magnet synchronous motor at time k+1.

[0040] The motor control module is configured to perform permanent magnet synchronous motor current control at time k+1 based on the predicted control current of the d-axis and q-axis of the permanent magnet synchronous motor after feedback correction, including a value function minimization stage and a converter.

[0041] The beneficial effects of this invention are:

[0042] (1) The current feedback correction control method of the permanent magnet synchronous motor model prediction of the present invention introduces feedback correction into the permanent magnet synchronous motor model. Without adding much program running burden, the accuracy of prediction will not be affected by factors such as nonlinearity, time variation, parameter accuracy and random disturbance of the controlled object itself. The dynamic and static steady-state control performance of the current of the permanent magnet synchronous motor is good.

[0043] (2) The current feedback correction control method of the permanent magnet synchronous motor model prediction of the present invention uses the particle swarm algorithm to tune the feedback correction coefficient. It uses three pieces of information, namely the current position, the global extreme value and the individual extreme value, to guide the particle to the next iteration position. The individual fully utilizes its own experience and the group experience to adjust its own state, which is fast and efficient in approaching the optimal solution. It can effectively optimize the parameters of the system, thereby improving the current steady-state control performance of the permanent magnet synchronous motor.

[0044] (3) The current feedback correction control method of the permanent magnet synchronous motor model prediction of the present invention allows the particle fitness to be selected as a single objective or multi-objective function according to the operation requirements. The present invention selects the square of the difference between the current reference value and the actual value as the particle fitness, which can achieve better static characteristics of the permanent magnet synchronous motor. Attached Figure Description

[0045] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0046] Figure 1 This is a flowchart illustrating the current feedback correction control method for permanent magnet synchronous motor model prediction according to the present invention.

[0047] Figure 2 This is a schematic diagram illustrating the process of tuning the feedback correction coefficient based on particle swarm optimization in one embodiment of the current feedback correction control method for permanent magnet synchronous motor model prediction according to the present invention. Detailed Implementation

[0048] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0049] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0050] This invention provides a current feedback correction control method for model prediction of permanent magnet synchronous motor. This method introduces feedback correction into the prediction model and determines the feedback correction coefficient through particle swarm optimization algorithm, thereby obtaining good dynamic and static performance.

[0051] The present invention provides a current feedback correction control method for model prediction of a permanent magnet synchronous motor, the control method comprising:

[0052] Based on the actual control currents of the permanent magnet synchronous motor on the d-axis and q-axis at the current time k, the predicted control currents of the permanent magnet synchronous motor on the d-axis and q-axis at time k+1 are predicted using the permanent magnet synchronous motor model.

[0053] Based on the actual and predicted control currents of the d-axis and q-axis of the permanent magnet synchronous motor from the initial time to the current time k, the feedback correction value at the current time k is obtained through a feedback correction model.

[0054] Based on the feedback correction value at the current time k and the predicted control currents of the permanent magnet synchronous motor at the d-axis and q-axis at the time k+1, the predicted control currents of the permanent magnet synchronous motor at the time k+1 after feedback correction are obtained.

[0055] Based on the predicted control currents of the permanent magnet synchronous motor at time k+1 after feedback correction, the current control of the permanent magnet synchronous motor at time k+1 is performed.

[0056] To more clearly explain the current feedback correction control method for permanent magnet synchronous motor model prediction of this invention, the following will be combined with... Figure 1 The steps in the embodiments of the present invention will be described in detail below.

[0057] The current feedback correction control method for permanent magnet synchronous motor model prediction according to the first embodiment of the present invention includes steps S10-S40, each step of which is described in detail below:

[0058] Step S10: Based on the actual control currents of the permanent magnet synchronous motor along the d-axis and q-axis at the current time k, predict the predicted control currents of the permanent magnet synchronous motor along the d-axis and q-axis at time k+1 using the permanent magnet synchronous motor model. and .

[0059] The d-axis and q-axis of a permanent magnet synchronous motor are the two axes of the dq coordinate system for the current feedback correction control of the permanent magnet synchronous motor.

[0060] The dq coordinate system is a synchronous rotating coordinate system obtained by rotating the static coordinate system formed by taking the axes of the three-phase symmetrical windings A, B, and C of the permanent magnet synchronous motor as the a, b, and c axes, respectively. The d-axis is the magnetic flux direction and the q-axis is the position that leads the d-axis by 90° electrical angle.

[0061] Step S20: Based on the actual control current and predicted control current of the permanent magnet synchronous motor along the d-axis and q-axis from the initial time to the current time k, the feedback correction value at the current time k is obtained through the feedback correction model, as shown in equation (1):

[0062] (1)

[0063] in, and For the current moment k Feedback correction value, and Each represents the current time. k The actual control currents of the d-axis and q-axis of a permanent magnet synchronous motor. and The current moment is obtained by predicting the current moment from the previous moment. k Predictive control currents for the d-axis and q-axis of a permanent magnet synchronous motor. Representatives will proceed with 1~ k Moment Summation, Representatives will proceed with 1~ k Moment Summation, and These are the feedback correction coefficients tuned using a particle swarm optimization method;

[0064] The tuning method for the feedback correction coefficients, which are tuned using the particle swarm optimization method, is as follows:

[0065] Construct a particle swarm containing N particles, and set the 4-dimensional target search space position parameters of each particle to be tuned. and The value of is shown in equation (2):

[0066] (2)

[0067] in, for In the particle swarm of particles, the first The position of each particle in the 4-dimensional target search space is represented. for In the particle swarm of particles, the first The four position parameters of each particle in the 4-dimensional target search space correspond to the parameters to be tuned. and .

[0068] The initial position and velocity of each particle are randomly assigned by the computer within a set range. The initial position and velocity of each particle are randomly initialized, based on the parameters to be tuned. and Due to the characteristics of the previous (sampling interval), when the sampling interval is very short, the sampling interval is short. (moment) and the next beat ( The predicted value and the actual value at the time are relatively close. It should be around 1, so the first search will be... The range is set to [0.5, 2]. The range is set to [0, 1e-3], and the particle flight speed range is set to [-0.05, 0.05]. The current command value is given by the upstream control system, i.e., the speed outer loop of the permanent magnet synchronous motor control system.

[0069] The number of particles (i.e., the value of N) and the number of cycles (i.e., the number of iterations) for optimization can be set according to the computer's performance. To prevent the optimization process from getting stuck in a local optimum, the number of particles can be appropriately increased during the first optimization.

[0070] The flight velocity of the i-th particle in a swarm of N particles is expressed as shown in equation (3):

[0071] (3)

[0072] in, for In the particle swarm of particles, the first The velocity of a particle in a 4-dimensional target search space is represented by... for In the particle swarm of particles, the first Four flight velocity parameters of a particle in a 4-dimensional target search space.

[0073] The particle corresponding to the tuning point and The value is substituted into the Simulink simulation model of the permanent magnet synchronous motor, and the Simulink simulation model of the permanent magnet synchronous motor is run to make the permanent magnet synchronous motor run stably under the actual operating conditions. The particle fitness is constructed as the square of the difference between the reference current value of the d-axis and q-axis and the actual current value when the permanent magnet synchronous motor is running stably, as shown in Equation (4):

[0074] (4)

[0075] in, For particle fitness, and These represent the d-axis command value and the actual current value during stable operation of the permanent magnet synchronous motor, respectively. and These are the q-axis command value and the actual current value when the permanent magnet synchronous motor is running stably.

[0076] When the i-th particle evolves from generation t to generation t+1, the particle with the smaller fitness is preferred, and the particle's position vector is updated as shown in equation (5):

[0077] (5)

[0078] in, For the updated number The next iteration In the particle swarm of particles, the first The position vectors of each particle. For the updated number The next iteration In the particle swarm of particles, the first The position vectors of each particle. For the first The next iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space. , representing the four dimensions of the particle's position vector and velocity vector, respectively.

[0079] No. The flight velocity vector of the i-th particle in the 4-dimensional target search space of the N particles in the next iteration. Its representation is shown in equation (6):

[0080] (6)

[0081] in, For the t-th iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space. The preset inertia coefficient, The flight step size of a particle toward its individual optimal value. The preset self-learning coefficient, This represents the step size of a particle's flight toward the global optimum. The preset global learning coefficients, and A random number in the range (0,1). For the first One particle in front The optimal value of the individual that minimizes the particle's fitness in the next iteration is located at this position. For all particles in front The location of the global optimal solution that minimizes the particle fitness in the next iteration. , representing the four dimensions of the particle's position vector and velocity vector, respectively.

[0082] In one embodiment of the present invention, the inertia coefficient is generally in the range of 0.5 to 0.8, the self-learning coefficient is generally in the range of 0.1 to 2, and the global learning coefficient is generally in the range of 0.1 to 2.

[0083] The particle fitness can be selected as a single-objective or multi-objective function according to the operational requirements. In order to achieve better static characteristics, this invention selects the square of the difference between the current reference value and the actual value as the fitness. If necessary, functions of operating state quantities such as voltage and speed can also be added for optimization. This invention will not be described in detail here.

[0084] The fitness of each particle is optimized iteratively until the set termination condition is met, and the tuned feedback correction coefficient is obtained.

[0085] The set termination condition can be that the loss value in the fitness optimization of the particle is lower than a set threshold or that a set number of optimization iterations is reached. In one embodiment of the present invention, the set termination condition is that the set number of optimization iterations is reached.

[0086] Step S30: Based on the feedback correction value at the current time k and the predicted control currents of the permanent magnet synchronous motor at the d-axis and q-axis at time k+1, obtain the predicted control currents of the permanent magnet synchronous motor at time k+1 after feedback correction, as shown in Equation (7):

[0087] (7)

[0088] in, and These are the predicted control currents of the permanent magnet synchronous motor at time k+1 after feedback correction, representing the d-axis and q-axis currents. and These are the predicted control currents of the permanent magnet synchronous motor along the d-axis and q-axis at time k+1, respectively. and These are the feedback correction values ​​for the current time k.

[0089] Step S40: Based on the predicted control current of the permanent magnet synchronous motor at time k+1 after feedback correction, perform current control of the permanent magnet synchronous motor at time k+1.

[0090] Will and The predicted control currents of the permanent magnet synchronous motor at time k+1 after feedback correction are output as actual predicted values ​​to the next step of minimizing the value function for model predictive control.

[0091] In this invention, the d-axis and q-axis predictive control currents of the permanent magnet synchronous motor are... and The current equation of a permanent magnet synchronous motor can be obtained from the motor parameters (including basic motor parameters such as stator resistance, stator inductance, and stator flux linkage), the motor state quantities at the current moment (including measurable state quantities such as stator current, rotor speed, and rotor position), and the control parameters (control time step).

[0092] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0093] The current feedback correction control system for permanent magnet synchronous motor model prediction according to the second embodiment of the present invention includes:

[0094] The current prediction module is configured to predict the d-axis and q-axis predicted control current of the permanent magnet synchronous motor at time k+1 based on the actual control current of the permanent magnet synchronous motor at the current time k and through the permanent magnet synchronous motor model.

[0095] The feedback correction value acquisition module is configured to obtain the feedback correction value at the current time k based on the actual control current and predicted control current of the d-axis and q-axis of the permanent magnet synchronous motor from the initial time to the current time k through the feedback correction model.

[0096] The feedback correction module is configured to obtain the d-axis and q-axis predicted control currents of the permanent magnet synchronous motor at time k+1 after feedback correction based on the feedback correction value at the current time k and the predicted control currents of the permanent magnet synchronous motor at time k+1.

[0097] The motor control module is configured to perform permanent magnet synchronous motor current control at time k+1 based on the predicted control current of the d-axis and q-axis of the permanent magnet synchronous motor after feedback correction, including a value function minimization stage and a converter.

[0098] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0099] It should be noted that the current feedback correction control system for permanent magnet synchronous motor model prediction provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0100] An electronic device according to a third embodiment of the present invention includes:

[0101] At least one processor; and

[0102] A memory communicatively connected to at least one of the processors; wherein,

[0103] The memory stores instructions that can be executed by the processor to implement the aforementioned current feedback correction control method based on the model prediction of the permanent magnet synchronous motor.

[0104] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described current feedback correction control method for model prediction of permanent magnet synchronous motor.

[0105] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the storage device and processing device described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0106] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0107] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0108] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0109] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A current feedback correction control method based on model prediction for a permanent magnet synchronous motor, characterized in that, The control method includes: Based on the current time k The actual control currents of the d-axis and q-axis of the permanent magnet synchronous motor are predicted using the permanent magnet synchronous motor model. k Predicted control currents of the permanent magnet synchronous motor on the d-axis and q-axis at time +1; Based on the initial time to the current time k The actual and predicted control currents of the d-axis and q-axis of the permanent magnet synchronous motor are obtained through a feedback correction model to determine the current moment. k The feedback correction value; The current time k The feedback correction value is obtained as follows: ； in, and For the current moment k Feedback correction value, and Each represents the current time. k The actual control currents of the d-axis and q-axis of a permanent magnet synchronous motor. and The current moment is obtained by predicting the current moment from the previous moment. k Predictive control currents for the d-axis and q-axis of a permanent magnet synchronous motor. Representatives will proceed with 1~ k Moment Summation, Representatives will proceed with 1~ k Moment Summation, and These are the feedback correction coefficients tuned using a particle swarm optimization method; Based on the current time k The feedback correction value and the k The predicted control currents of the permanent magnet synchronous motor along the d-axis and q-axis at time +1 are obtained after feedback correction. k Predicted control currents of the permanent magnet synchronous motor on the d-axis and q-axis at time +1; Based on the feedback correction k The predicted control currents of the permanent magnet synchronous motor along the d-axis and q-axis at time +1 are then analyzed. k Current control of permanent magnet synchronous motor at time +1.

2. The current feedback correction control method for permanent magnet synchronous motor model prediction according to claim 1, characterized in that, The d-axis and q-axis of the permanent magnet synchronous motor are the two axes of the dq coordinate system for the current feedback correction control of the permanent magnet synchronous motor. The dq coordinate system is a synchronous rotating coordinate system obtained by rotating the static coordinate system formed by taking the axes of the three-phase symmetrical windings of the permanent magnet synchronous motor (A, B, and C) as the a, b, and c axes, respectively, with the magnetic flux direction as the d axis and the position 90° electrical angle ahead of the d axis as the q axis.

3. The current feedback correction control method for permanent magnet synchronous motor model prediction according to claim 1, characterized in that, The feedback correction coefficient, tuned using the particle swarm optimization method, is tuned as follows: Build includes N A particle swarm of particles, with the 4D target search space position parameters of each particle set to be tuned. and The value; Randomly initialize the initial position and initial velocity of each particle, according to the parameters to be tuned. and The characteristics of the first search will be The range is set to [0.5, 2]. , The range is set to [0, 1e-3], the particle flight velocity range is set to [-0.05, 0.05], and the feedback correction value is... and The amplitude limit is set to [-1, 1]; The particle corresponding to the tuning point and The values ​​are substituted into the Simulink simulation model of the permanent magnet synchronous motor, and the Simulink simulation model of the permanent magnet synchronous motor is run to make the permanent magnet synchronous motor run stably under the actual operating conditions. The particle fitness is constructed as the square of the difference between the reference current values ​​of the d-axis and q-axis and the actual current values ​​when the permanent magnet synchronous motor is running stably. The i-th particle is generated by the th t Dai Xiangdi t During the +1 generation evolution, particles with lower fitness are preferred, and their position vectors are updated accordingly. The fitness of each particle is optimized iteratively until the set termination condition is met, and the tuned feedback correction coefficient is obtained.

4. The current feedback correction control method for permanent magnet synchronous motor model prediction according to claim 3, characterized in that, The step involves setting the 4D target search space position parameters of each particle as to be tuned. and The value is obtained by: ； in, for In the particle swarm of particles, the first The position of each particle in the 4-dimensional target search space is represented. for In the particle swarm of particles, the first The four position parameters of each particle in the 4-dimensional target search space correspond to the parameters to be tuned. and .

5. The current feedback correction control method for permanent magnet synchronous motor model prediction according to claim 4, characterized in that, The In the particle swarm of particles, the first There are 1 particle whose flight speed is: ； in, for In the particle swarm of particles, the first The velocity of a particle in a 4-dimensional target search space is represented by... for In the particle swarm of particles, the first Four flight velocity parameters of a particle in a 4-dimensional target search space.

6. The current feedback correction control method for permanent magnet synchronous motor model prediction according to claim 5, characterized in that, The method for constructing particle fitness as the square of the difference between the reference current value and the actual current value of the d-axis and q-axis of the permanent magnet synchronous motor is as follows: ； in, For particle fitness, and These represent the d-axis command value and the actual current value during stable operation of the permanent magnet synchronous motor, respectively. and These are the q-axis command value and the actual current value when the permanent magnet synchronous motor is running stably.

7. The current feedback correction control method for permanent magnet synchronous motor model prediction according to claim 6, characterized in that, The method for updating the particle's position vector, prioritizing particles with lower fitness, is as follows: ； in, For the updated number The next iteration In the particle swarm of particles, the first The position vectors of each particle. For the updated number The next iteration In the particle swarm of particles, the first The position vectors of each particle. For the first The next iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space. , representing the four dimensions of the particle's position vector and velocity vector, respectively.

8. The current feedback correction control method for permanent magnet synchronous motor model prediction according to claim 7, characterized in that, The first The next iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space It is represented as: ； in, For the t-th iteration In the particle swarm of particles, the first The velocity vector of each particle in the 4-dimensional target search space. The preset inertia coefficient, The flight step size of a particle toward its individual optimal value. The preset self-learning coefficient, This represents the step size of a particle's flight toward the global optimum. The preset global learning coefficients, and A random number in the range (0,1). For the first One particle in front The optimal value of the individual that minimizes the particle's fitness in the next iteration is located at this position. For all particles in front The location of the global optimal solution that minimizes the particle fitness in the next iteration. , representing the four dimensions of the particle's position vector and velocity vector, respectively.

9. A current feedback correction control system for model prediction of a permanent magnet synchronous motor, based on the current feedback correction control method for model prediction of a permanent magnet synchronous motor according to any one of claims 1-8, characterized in that, The control system includes: The current prediction module is configured to predict based on the current time. k The actual control currents of the d-axis and q-axis of the permanent magnet synchronous motor are predicted using the permanent magnet synchronous motor model. k Predicted control currents of the permanent magnet synchronous motor on the d-axis and q-axis at time +1; The feedback correction value acquisition module is configured to be based on the time from the initial time to the current time. k The actual and predicted control currents of the d-axis and q-axis of the permanent magnet synchronous motor are obtained through a feedback correction model to determine the current moment. k The feedback correction value; The feedback correction module is configured to be based on the current time. k The feedback correction value and the k The predicted control currents of the permanent magnet synchronous motor along the d-axis and q-axis at time +1 are obtained after feedback correction. k Predicted control currents of the permanent magnet synchronous motor on the d-axis and q-axis at time +1; The motor control module is configured to be based on the feedback correction. k The predicted control currents of the permanent magnet synchronous motor along the d-axis and q-axis at time +1 are then analyzed. k Current control of permanent magnet synchronous motor at time +1.

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