Permanent magnet synchronous motor vector control method for intelligently optimizing rotating speed ring PI controller parameters

Abstract

The invention relates to the technical field of permanent magnet synchronous motor control, in particular to a permanent magnet synchronous motor vector control method for optimizing rotating speed ring PI controller parameters through an intelligent algorithm. The method comprises the following steps: establishing a double-closed-loop vector control system model, and setting parameters Kp and Ki of a PI controller by improving a sea gull optimization algorithm (ISOA). The improved seagull optimization algorithm initializes a population position, a cosine type motion behavior and a Levy flight strategy by combining Logistic-Tent chaotic mapping so as to enhance global and local search capabilities and improve motor response speed and control precision. And the fitness function adopts an ITAE criterion, so that the rotating speed control error is effectively inhibited. Simulation results show that the method can significantly reduce overshoot and adjustment time, improves system stability, and has obvious advantages compared with traditional PI control.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet synchronous motor control, and in particular to a permanent magnet synchronous motor vector control method using an intelligent algorithm to optimize speed loop PI controller parameters. Background Art

[0002] With the continuous development of my country's industrial technology, permanent magnet synchronous machine (PMSM) has been widely used due to its advantages such as low loss, simple structure and high efficiency. However, PMSM has the characteristics of multivariable, nonlinear and strong coupling. It will be affected by various interferences during actual operation, resulting in a decrease in the control performance of the system. In order to obtain better control performance, it is a practical method to adopt a good control method without improving the hardware performance of the motor. In engineering applications, the most widely used PMSM control system is the PI controller. The PI controller has the advantages of simple method and convenient regulation, but it is difficult to meet the control requirements of the motor when facing nonlinear complex systems. Therefore, scholars at home and abroad combine various intelligent algorithms with PI control to obtain better control performance.

[0003] Typical intelligent optimization algorithms include genetic algorithms, simulated annealing algorithms, particle swarm optimization algorithms, etc. Among them, Dhiman et al. proposed the Seagull Optimization Algorithm (SOA) in 2019. This algorithm simulates the migration behavior of seagulls and the attack behavior of prey to build a theoretical model to solve the target problem. Compared with the ant colony optimization algorithm and the gray wolf optimization algorithm, SOA has the advantages of fewer parameters, strong optimization ability, and easy implementation. The algorithm has been successfully applied to signal processing, system control, engineering design, truss design and other problems. However, similar to other intelligent optimization algorithms, the standard SOA still has problems such as slow convergence and easy to fall into local optimal solutions. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in order to solve the problems existing in the prior art in the above-mentioned background technology, a permanent magnet synchronous motor vector control method is provided which optimizes the speed loop PI controller parameters with an intelligent algorithm.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters, comprising the following steps: S1. Establish a dual closed-loop vector control system model for permanent magnet synchronous motor speed and current; S2. Control parameters of the speed loop PI controller using the improved Seagull optimization algorithm K p, K i The tuning is performed and a suitable fitness function is selected to evaluate whether the PI controller parameters in the permanent magnet synchronous motor vector control system are optimal parameters. The optimal solution is obtained through iteration to achieve efficient control of the permanent magnet synchronous motor.

[0006] Furthermore, in step S2, the Seagull optimization algorithm is improved to optimize the speed loop PI controller, specifically: S21, the proportional gain in the speed loop PI controller parameters K p , integral gain K i As the position of the seagulls, the position of the population is initialized using Logistic-Tent chaotic mapping; S22. Calculate the fitness value of the population and determine the individual extreme value of the seagull P best , the global extreme value G best ; S23, seagull migration phase, using cosine-type movement behavior to update additional variables A , update the convergence direction of the seagull M s , a random number that balances global and local searches B and the new location of the seagull D s ; S24, seagull attacking prey stage, introduce Levi flight strategy to update the position of seagull after attacking prey P s ( t ); S25, using the fitness function to calculate the difference between the actual speed and the set speed of the permanent magnet synchronous motor to obtain the new fitness value of each seagull, and update the global extreme value G best ; S26: Determine whether the optimization stop condition is met. If not, return to step S23 to continue iteration; if the condition is met, stop the optimization and output the global extreme value. G best Corresponding seagull position.

[0007] Furthermore, in step S21, the position of the population is initialized by Logistic-Tent chaotic mapping, specifically:

[0008] in, β is the damping coefficient, P n is the number of pole pairs of the motor, φf is the permanent magnetic flux linkage, J is the moment of inertia of the motor; Defining parameters r and the initial sequence x 0, parameters r is a constant value in the interval [0,1], the initial sequence x 0 is the initial value corresponding to the individual position of the population of the Seagull optimization algorithm; Randomly generate initial value sequence x In the range of 0 x value, let x 0= x 0( n ),in n =1,2,3,…, x ; Will x 0 as a sequence x The first value of x 0 is the n The state value at the moment is usually in [0,1], and then the subsequent value is generated according to the Logistic-Tent chaotic mapping formula, where the Logistic-Tent chaotic mapping formula is:

[0009] In the formula, the parameters r is a constant in [0,1]; Save the resulting sequence x , convert the sequence x As the initial position of the population individuals of the Seagull optimization algorithm.

[0010] Furthermore, in step S22, the individual extreme value P of the seagull is determined. best , the global extremum G best Specifically: Calculate the extreme value of each individual according to the initial position X of the population individual of the Seagull optimization algorithm fitness , the calculation formula is as follows:

[0011] In each iteration, the fitness values ​​of all individual particles are calculated and compared with the currently recorded global optimal fitness value; the individual extreme value P best is the value of the objective function obtained by the particle at the best position G best It is the maximum or minimum value among the individual extreme values ​​of all particles.

[0012] Furthermore, in step S23, the cosine motion behavior is introduced to improve the seagull position update in the migration phase of the seagull optimization algorithm. The formula of the cosine improved motion behavior using a nonlinear strategy is:

[0013] Where: A ( t ) is a modified motor behavior, T is the maximum number of iterations, n To adjust the index.

[0014] Furthermore, in step S24, the Levy flight strategy is introduced to improve the position update of the seagull after attacking the prey in the attack prey phase of the seagull optimization algorithm. The updated position formula is:

[0015] In the formula, x , y , z Describe the motion behavior of seagulls on a flat surface. D s ( t ) is the position of the seagulls during the migration phase, P s ( t ) is the position of the seagull after attacking the prey, P bs ( t ) is the best position of the current seagull, L ( λ ) represents the path force that follows the Levy distribution and satisfies: , In the formula, s is the random step length of Levy flight.

[0016] Furthermore, the step length s The expression is: , In the formula, u and ν It obeys the random normal distribution, expressed as:

[0017] In the formula, β is a constant between [0,2], is the standard gamma distribution, is the standard deviation, is the variance.

[0018] Furthermore, in step S25, the time multiplied absolute error integral criterion is used to calculate the fitness value of the population to suppress the error in the permanent magnet synchronous motor speed control. The calculation formula is as follows:

[0019] In the formula, e ( t )for t The difference between the actual motor speed and the set speed at that moment.

[0020] Beneficial effects of the present invention: (1) The permanent magnet synchronous motor control strategy based on the ISOA algorithm can effectively improve the response speed of the permanent magnet synchronous motor, shorten the adjustment time and reduce the overshoot; (2) The Logistic-Tent chaotic mapping is used to initialize the population position. Compared with the randomly initialized population position, the improved population initial position distribution is more uniform, which increases the diversity of the population and is conducive to improving the optimization efficiency of the algorithm. (3) Using cosine motion behavior to update the additional variable A can improve the global search capability in the early iteration and the local search capability in the later iteration; (4) Integrating the Levy flight strategy is conducive to enhancing the diversity of the population in the early stage of the search, reducing the risk of falling into the local optimum, and improving the quality of optimization in the later stage of the search; (5) Selecting the time multiplied by the absolute error integral criterion as the fitness function indicator of the ISOA algorithm can effectively suppress the long-term errors in the system; (6) An improved Seagull optimization algorithm is proposed by combining the Seagull optimization algorithm, Logistic-Tent chaotic mapping, cosine motion behavior and Levy flight strategy. This algorithm can improve the problems of uneven population initialization, poor global optimization ability in the early iteration and difficulty in escaping the local optimal solution in the later stage. By searching for the optimal parameters of the speed loop PI controller, the response speed of the permanent magnet synchronous motor is improved, the adjustment time is reduced and the overshoot is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0022] Figure 1 It is a flow chart of a permanent magnet synchronous motor vector control system for intelligently optimizing speed loop PI controller parameters of the present invention.

[0023] Figure 2 The present invention is a flow chart of a method for a permanent magnet synchronous motor vector control system for intelligently optimizing speed loop PI controller parameters.

[0024] Figure 3 It is the bifurcation diagram of the Logistic-Tent chaotic mapping in the present invention.

[0025] Figure 4 yes Figure 3 The corresponding spatial distribution histogram.

[0026] Figure 5 It is the random number generated by the rand function in the present invention.

[0027] Figure 6 yes Figure 5 The corresponding spatial distribution histogram.

[0028] Figure 7 This is a comparison chart of the speed curves under traditional PI control and PI control optimized by ISOA algorithm. DETAILED DESCRIPTION

[0029] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0030] like Figure 1 The permanent magnet synchronous motor vector control system with intelligent optimization of speed loop PI controller parameters shown in the figure includes a rotor position sensor, SVPWM pulse width modulation, a converter, a current detection device, a PMSM, an ISOA algorithm to optimize the speed loop PI controller, a Clark transformation module, a Park transformation module, and an inverse Park transformation module. Among them, the Clark transformation module is used to convert the three-phase current or voltage into components in a two-phase stationary coordinate system, so as to prepare for the subsequent Park transformation. The Park transformation module is used to convert the current and voltage of the three-phase AC motor from a stationary coordinate system to a rotating coordinate system, so that the control of the current and voltage is similar to the control of the DC signal, and a simple PI controller is used for adjustment. The inverse Park transformation module is used to convert the control result from the two-phase rotating coordinate system back to the two-phase stationary coordinate system, and then further converted back to the three-phase current through the inverse Clark transformation module for driving the motor.

[0031] In this system, the three-phase stator current of the permanent magnet synchronous motor is first collected by the current sensor. , , , after Clark transformation, we get the permanent magnet synchronous motor , Actual stator current of the axis , ,Will , And the estimated rotor angle obtained by the sliding mode observer The permanent magnet synchronous motor is obtained by Park transformation , Actual stator current of the shaft , ; Given a permanent magnet synchronous motor The axis reference current is ,but Shaft current deviation , e-axis current deviation value ,Will Shaft current deviation The d-axis PI controller is used to obtain Coupling reference voltage of the axis , and then Shaft current deviation pass The axis PI controller obtains the axis coupling reference voltage ,Will Axis reference voltage , After the inverse Park transform module, we get , Axis reference voltage , , and then , It is sent to the SVPWM module to generate a three-phase PWM signal, which then controls the on and off of the three-phase inverter to drive the permanent magnet synchronous motor.

[0032] The control method of the permanent magnet synchronous motor vector control system with intelligent optimization of the speed loop PI controller parameters is as follows: firstly, a permanent magnet synchronous motor speed and current double closed-loop vector control system model is established; then, the control parameters of the speed loop PI controller are adjusted by using the improved Seagull optimization algorithm. K p , K i The tuning is performed and a suitable fitness function is selected to evaluate whether the PI controller parameters in the permanent magnet synchronous motor vector control system are optimal parameters. The optimal solution is obtained through iteration to achieve efficient control of the permanent magnet synchronous motor.

[0033] like Figure 2 As shown in the figure, the improved seagull optimization algorithm optimizes the speed loop PI controller, specifically: first, the Logistic-Tent chaotic map is combined to initialize the population position, which improves the diversity of the population and is conducive to improving the optimization efficiency of the algorithm; then, combined with the cosine motion behavior, the additional variable A is updated during the seagull migration stage, which improves the global search ability in the early iteration and the local search ability in the later iteration; then, the Levy flight strategy is adopted to update the position during the seagull attacking prey stage, which enhances the population diversity in the early search stage, reduces the risk of falling into the local optimum, and improves the quality of optimization in the later search stage. Finally, ITAE (time multiplied by the integral of absolute error criterion) is used as the fitness function to effectively suppress the speed control error.

[0034] To be more specific, firstly, the speed loop PI controller parameters Kp and Ki are used as the positions of the seagulls, and the positions of the population are initialized using the Logistic-Tent chaotic mapping; The digital expression of the classic PI algorithm of the speed loop is:

[0035] In the formula, u ( k ) is the controller output, e ( k ) is the error, K p is the proportionality coefficient, K i is the integration coefficient, k is the sampling sequence.

[0036] Among them, the specific steps of initializing the population through Logistic-Tent chaotic mapping are: First, the position of the population is initialized through the Logistic-Tent chaotic map, specifically:

[0037] in, β is the damping coefficient, P n is the number of pole pairs of the motor, φ f is the permanent magnetic flux linkage, J is the moment of inertia of the motor; Redefine parameters r and the initial sequence x 0, parameters r is a constant value in the interval [0,1], the initial sequence x 0 is the initial value corresponding to the individual position of the population of the Seagull optimization algorithm; Randomly generate initial value sequence x In the range of 0 x value, let x 0= x 0( n ),in n =1,2,3,…, x ; Will x 0 as a sequence x The first value of x 0 is the n The state value at the moment is usually in [0,1], and then the subsequent value is generated according to the Logistic-Tent chaotic mapping formula. The Logistic-Tent chaotic mapping formula is:

[0038] In the formula, the parameters r is a constant in [0,1]. rTake 0.3; Save the resulting sequence x , convert the sequence x As the initial position of the population individuals of the Seagull optimization algorithm.

[0039] The Seagull optimization algorithm usually uses random number method to initialize the population. When solving complex optimization problems, there are defects such as reduced population diversity in the later stage of search and easy to fall into local optimality. Logistic-Tent chaotic mapping has good pseudo-random characteristics and is sensitive to initial parameters. Using Logistic-Tent chaotic mapping instead of random initialization of the Seagull optimization algorithm can make the population resources more evenly distributed in the search space and enrich the diversity of the population.

[0040] like Figure 3 and Figure 5 As shown in the figure, the population size is set to 5000, and two methods (Logistic-Tent chaotic mapping and rand function) are used to generate chaotic sequences in the interval [0,1]. The rand function is a commonly used random number generation function, which is used to generate uniformly distributed random numbers in a specified interval. The random numbers generated by the rand function are used to initialize the population position of the Seagull optimization algorithm. Here, the rand function is used to compare with the Logistic-Tent chaotic mapping to show the advantages of the Logistic-Tent chaotic mapping in initializing the population position. The corresponding spatial distribution histogram, such as Figure 4 and Figure 6 As shown, it can be seen that the Logistic-Tent chaotic mapping is relatively evenly distributed in the feasible domain.

[0041] Then, calculate the fitness value of the population and determine the individual extreme values ​​of the seagulls P best , the global extreme value G best . Calculate the extreme value of each individual according to the initial position X of the population individual of the Seagull optimization algorithm fitness , the calculation formula is as follows:

[0042] In each iteration, the fitness values ​​of all individual particles are calculated and compared with the currently recorded global optimal fitness value; the individual extreme value P best is the value of the objective function obtained by the particle at the best position G best It is the maximum or minimum value among the individual extreme values ​​of all particles.

[0043] During the seagull migration phase, additional variables are updated using cosine-type movement behavior A , update the convergence direction of the seagull Ms , a random number that balances global and local searches B and the new location of the seagull D s ; Convergence direction M s , random number B New location with Seagull D s The mathematical expression is:

[0044]

[0045]

[0046] In the formula, t is the current iteration number, r d is a random number in [0,1], P bs ( t ) indicates the best position of the current seagull, P s ( t ) represents the current position of the seagull, C s ( t ) represents a position that does not conflict with other seagulls, and its update expression is as follows:

[0047] Additional variables A The cosine motion behavior is updated, and its expression is:

[0048] Where: A ( t ) is a modified motor behavior, t is the current iteration number, T is the maximum number of iterations, n To adjust the index, the maximum number of iterations in this example T =30, adjustment index n =0.4.

[0049] During the seagull migration phase, A ( t ) represents the movement of the seagull in the search space during the collision avoidance process. A ( t) changes linearly and decreases with the increase of iteration times, which is not conducive to global search and the algorithm accuracy is not high enough. The cosine motion behavior is used to improve the migration stage of the seagull algorithm, so that the seagull's movement distance in the early stage is larger, which can better perform global search, and the seagull's movement distance is reduced in the later stage to improve the search accuracy.

[0050] During the seagull attacking prey stage, the Levy flight strategy is introduced to update the position of the seagull after attacking the prey. P s ( t ). The Levy flight strategy is introduced to improve the position update of the seagull after attacking the prey in the attack prey phase of the seagull optimization algorithm. The updated position formula is:

[0051] In the formula, x , y , z Describe the motion behavior of seagulls on a flat surface. D s ( t ) is the position of the seagulls during the migration phase, P s ( t ) is the position of the seagull after attacking the prey, P bs ( t ) is the best position of the current seagull, L ( λ ) represents the path force that follows the Levy distribution and satisfies:

[0052] In the formula, s is the random step length of Levy flight.

[0053] Step Length s The expression is:

[0054] In the formula, u and ν It obeys the random normal distribution and can be expressed as:

[0055] In the formula, β is a constant between [0,2], is a standard gamma distribution. In this example, β Take 1.5.

[0056] Lévy flight is convenient and random. It is a non-Gaussian random process. Its stable increment obeys the Lévy stable distribution. Its flight trajectory has the characteristics of a random walk, consisting of small-step jumps and occasional large-step jumps, which alternate with each other. Lévy flight is integrated into the position update of the seagull attacking prey in the SOA algorithm. In the early search, a large step size can expand the search range, which is conducive to enhancing the diversity of the population and reducing the risk of the seagull algorithm falling into the local optimum. In the later search process, a small step size can improve the accuracy of the algorithm solution.

[0057] Next, the difference between the actual speed and the set speed of the permanent magnet synchronous motor is calculated using the fitness function to obtain the new fitness value of each seagull and update the global extreme value. G best ; Using the time multiplied absolute error integral criterion ( ITAE ) is used to calculate the fitness value of the population and suppress the error in the speed control of the permanent magnet synchronous motor. The expression is as follows:

[0058] In the formula, e ( t )for t The difference between the actual motor speed and the set speed at that moment.

[0059] Finally, determine whether the optimization stop condition is met. If not, integrate the cosine operation behavior to update the seagull's migration position, integrate the Levy flight strategy to update the seagull's optimal attack position, and then re-decompose and assign the speed loop PI controller parameters Kp and Ki; if the conditions are met, stop the optimization and output the global extreme value G best Corresponding seagull position.

[0060] The simulation is carried out based on the Simulink platform, where the relevant parameters of the PMSM are shown in Table 1.

[0061] Table 1 PMSM parameters

[0062] In order to verify the improvement of the ISOA algorithm on the vector control performance of permanent magnet synchronous motor, the simulation results of the ISOA algorithm and the traditional PI control are compared. Figure 5 As shown. Among them, the population number of the ISOA algorithm is set to 30, the maximum number of iterations is set to 30, and the PMSM set speed is set ω * = 3000r / min, no-load start, the speed suddenly changes to 2000r / min at 0.025s. The optimal parameters of the speed loop PI controller in this example K p = 0.739, Ki = 0.453. Figure 5 It can be seen that during no-load starting, the traditional PI control has a larger overshoot and a longer adjustment time than the ISOA algorithm control. When the speed changes suddenly, the permanent magnet synchronous motor vector control system under the ISOA algorithm control can reach the set value more stably.

[0063] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters, characterized in that: The following steps are involved: S1. Establish a dual closed-loop vector control system model for permanent magnet synchronous motor speed and current; S2. Control parameters of the speed loop PI controller using the improved Seagull optimization algorithm K p , K i The tuning is performed and a suitable fitness function is selected to evaluate whether the PI controller parameters in the permanent magnet synchronous motor vector control system are optimal parameters. The optimal solution is obtained through iteration to achieve efficient control of the permanent magnet synchronous motor.

2. The permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters according to claim 1 is characterized in that: In step S2, the Seagull optimization algorithm is improved to optimize the speed loop PI controller, specifically: S21, the proportional gain in the speed loop PI controller parameters K p , integral gain K i As the position of the seagulls, the position of the population is initialized using Logistic-Tent chaotic mapping; S22. Calculate the fitness value of the population and determine the individual extreme value of the seagull P best , the global extreme value G best ; S23, seagull migration phase, using cosine-type movement behavior to update additional variables A , update the convergence direction of the seagull M s , a random number that balances global and local searches B and the new location of the seagull D s ; S24, seagull attacking prey stage, introduce Levi flight strategy to update the position of seagull after attacking prey P s ( t ); S25, using the fitness function to calculate the difference between the actual speed and the set speed of the permanent magnet synchronous motor to obtain the new fitness value of each seagull, and update the global extreme value G best ; S26: Determine whether the optimization stop condition is met. If not, return to step S23 to continue iteration; if the condition is met, stop the optimization and output the global extreme value. G best Corresponding seagull position.

3. The permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters according to claim 2 is characterized in that: In step S21, the position of the population is initialized by Logistic-Tent chaotic mapping, specifically: in, β is the damping coefficient, P n is the number of pole pairs of the motor, φ f is the permanent magnetic flux linkage, J is the moment of inertia of the motor; Defining parameters r and the initial sequence x 0, parameters r is a constant value in the interval [0,1], the initial sequence x 0 is the initial value corresponding to the individual position of the population of the Seagull optimization algorithm; Randomly generate initial value sequence x In the range of 0 x value, let x 0= x 0( n ),in n =1,2,3,…, x ; Will x 0 as a sequence x The first value of x 0 is the n The state value at the moment is usually in [0,1], and then the subsequent value is generated according to the Logistic-Tent chaotic mapping formula, where the Logistic-Tent chaotic mapping formula is: In the formula, the parameters r is a constant in [0,1]; Save the resulting sequence x , convert the sequence x As the initial position of the population individuals of the Seagull optimization algorithm.

4. The permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters according to claim 2 is characterized in that: In step S22, the individual extreme value P of the seagull is determined best , the global extremum G best Specifically: Calculate the extreme value of each individual according to the initial position X of the population individual of the Seagull optimization algorithm fitness , the calculation formula is as follows: In each iteration, the fitness values ​​of all individual particles are calculated and compared with the currently recorded global optimal fitness value; the individual extreme value P best is the value of the objective function obtained by the particle at the best position G best It is the maximum or minimum value among the individual extreme values ​​of all particles.

5. The permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters according to claim 2 is characterized in that: In step S23, the cosine type motion behavior is introduced to improve the seagull position update in the migration phase of the seagull optimization algorithm. The formula of the cosine improved motion behavior using a nonlinear strategy is: Where: A ( t ) is an improved motor behavior, T is the maximum number of iterations, n To adjust the index.

6. The permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters according to claim 2 is characterized in that: In step S24, the Levy flight strategy is introduced to improve the position update of the seagull after attacking the prey in the attack prey phase of the seagull optimization algorithm. The updated position formula is: In the formula, x , y , z Describe the motion behavior of seagulls on a flat surface. D s ( t ) is the position of the seagulls during the migration phase, P s ( t ) is the position of the seagull after attacking the prey, P bs ( t ) is the best position of the current seagull, L ( λ ) represents the path force that follows the Levy distribution and satisfies: ， In the formula, s is the random step length of Levy flight.

7. The permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters according to claim 5 is characterized in that: Step Length s The expression is: ， In the formula, u and ν It obeys the random normal distribution, expressed as: In the formula, β is a constant between [0,2], is the standard gamma distribution, is the standard deviation, is the variance.

8. The permanent magnet synchronous motor vector control method for intelligently optimizing the speed loop PI controller parameters according to claim 2 is characterized in that: In step S25, the time multiplied absolute error integral criterion is used to calculate the fitness value of the population to suppress the error in the speed control of the permanent magnet synchronous motor. The calculation formula is as follows: In the formula, e ( t )for t The difference between the actual motor speed and the set speed at that moment.

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