Permanent magnet synchronous motor speed regulation control method based on improved hawk algorithm

Abstract

The invention discloses a permanent magnet synchronous motor speed regulation control method based on an improved hawk algorithm. The method comprises the following steps: acquiring permanent magnet synchronous motor data; inputting the permanent magnet synchronous motor data into a pre-trained permanent magnet synchronous motor speed regulation control model, and outputting a speed regulation result of the permanent magnet synchronous motor; and the permanent magnet synchronous motor speed regulation control model is trained by using an improved Tianying algorithm. According to the method, the problem of controller parameter adjustment in a permanent magnet synchronous motor speed regulation control model is solved, the improved hawk optimization algorithm is introduced, the algorithm realizes optimal configuration of global search and local search capabilities by virtue of the characteristics of rapid convergence, high global search capability and high solving precision, and the optimal configuration of the global search and local search capabilities is realized in the parameter optimization process. A dynamic simplex method strategy and an annealing simulation-gold sine updating strategy are utilized to expand the optimal selection range, the algorithm shows excellent performance, and the labor intensity of researchers for adjusting parameters is remarkably relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a permanent magnet synchronous motor speed regulation control method based on an improved Sky Eagle algorithm. Background Art

[0002] With the rapid development of industrial automation and robotics, permanent magnet synchronous motors are becoming more and more popular in diverse fields such as manufacturing, aerospace, and medical care. The research on its control algorithm continues to deepen, and innovative algorithms and technologies continue to emerge, aiming to achieve efficient and high-performance operation of the motor and flexibly respond to various working conditions and load changes. The accuracy of motor speed control is directly related to production efficiency, product quality, and operational safety. In actual application scenarios, the speed control system of permanent magnet synchronous motors faces many challenges, including the nonlinear characteristics of the system, time-varying characteristics, model uncertainty, and interference from the external environment. These factors pose a severe test to the accuracy of speed control and system stability, prompting researchers to continuously explore more advanced control strategies and optimization methods to overcome these difficulties and promote permanent magnet synchronous motor technology to a higher level.

[0003] Traditional permanent magnet synchronous motor speed control strategies, such as PID, sliding mode and anti-disturbance control, can meet basic speed control requirements, but when dealing with complex challenges such as system nonlinearity, time-varying characteristics, model inaccuracy and external disturbances, their control accuracy and efficiency are difficult to achieve precise and efficient control. As a cutting-edge control technology, STA-CADRC control can capture and compensate for the internal dynamic changes and external disturbances of the system in real time by introducing its cascade extended state observer, thereby significantly improving the system's anti-interference ability and control accuracy. The performance optimization of STA-CADRC control is deeply affected by its fine adjustment of parameters. Unfortunately, the parameter tuning process is complex and challenging, and traditional methods often find it difficult to directly find the optimal parameter combination, which limits the control effect of the algorithm in practical applications. Therefore, exploring efficient and accurate parameter tuning strategies to overcome this problem has become one of the key directions for future research in this field. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a permanent magnet synchronous motor speed control method based on an improved Sky Eagle algorithm, which can solve the parameter adjustment problem of the controller in the permanent magnet synchronous motor speed control model.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] On the one hand, the present invention provides a permanent magnet synchronous motor speed control method based on an improved Sky Eagle algorithm, comprising:

[0007] Get permanent magnet synchronous motor data;

[0008] According to the permanent magnet synchronous motor data, based on a pre-trained permanent magnet synchronous motor speed control model, a speed regulation result of the permanent magnet synchronous motor is obtained;

[0009] The permanent magnet synchronous motor speed control model is trained using the improved Sky Eagle algorithm including:

[0010] Acquire a population of eagles, and calculate an initial flight position of each eagle in the population of eagles;

[0011] In the expansion exploration phase, the initial flight position of the Eagle is used to update the flight position of the Eagle. In the reduction exploration phase, the flight area of ​​the Eagle is updated. In the expansion development phase, the Eagle's predation area is updated. In the reduction development phase, the initial flight position of the Eagle is used to update the attack position of the Eagle. In the expansion exploration phase, the dynamic simplex method strategy is used to expand the optimal range of the Eagle's flight position. In the expansion development phase, the annealing simulation-golden sine update strategy is used to expand the optimal range of the Eagle's predation area.

[0012] According to the preset target prey function, the untrained permanent magnet synchronous motor speed control model is used to obtain the optimal flight position and attack position of the Skyhawk; the parameters represented by the flight position and attack position of the optimal Skyhawk are used as the parameters of the permanent magnet synchronous motor speed control model.

[0013] Optionally, the initial flight position of each eagle in the eagle population is calculated as follows:

[0014] ;

[0015] In the formula, Indicates The initial flight position of the Skyhawk at the iteration; represents the lower bound of the eagle population; Indicates that it is located A random value between ; Represents the upper bound of the eagle population.

[0016] Optional, when and When , the Eagle is in the expansion exploration stage. In the expansion exploration stage, the Eagle's initial flight position is used to update the Eagle's flight position. The formula is:

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] In the formula, Indicates In the first iteration, the dynamic simplex method strategy is used to update the flight position of the eagle; Indicates the upper bound of Tianying in the dim-th spatial dimension; Indicates the lower bound of Tianying in the dim-th spatial dimension; represents the dynamic scaling factor; Indicates The flight position of the Skyhawk at the iteration; Indicates the current iteration number; Indicates the maximum number of iterations; Indicates The optimal flight position of the Skyhawk at the iteration; Indicates The average flight position of the Skyhawk at the iteration; Indicates the number of Skyhawks.

[0022] Optional, when and When , the Eagle is in the shrinking exploration phase. In the shrinking exploration phase, the Eagle's flight area is updated. The formula is:

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] In the formula, Indicates the current iteration number; Indicates the maximum number of iterations; Indicates that it is located A random value between ; Indicates The flight area of ​​the Skyhawk during the first iteration; Indicates The optimal flight position of the Skyhawk at the iteration; Indicates that the Eagle is Flight distribution function of the problem dimension; Indicates The flight position of a hawk randomly selected from the hawk population in the first iteration; represents the first intermediate parameter; represents the second intermediate parameter; represents the first constant; Indicates that it is located A random value between ; Indicates flight dynamic parameters; Indicates that it is located A random value between ; represents the gamma function; represents the second constant; Indicates the search step length; Indicates the search angle; Indicates the number of search operations; represents the third constant; express Integers in Represents spatial dimension; represents the fourth constant; express A random value between .

[0028] Optional, when and When , the Eagle is in the expansion and development stage. In the expansion and development stage, the Eagle's prey area is updated. The formula is:

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] In the formula, Indicates the current iteration number; Indicates the maximum number of iterations; Indicates that it is located A random value between ; Indicates In the second iteration, the annealing simulation-golden sine strategy is used to update the hunting area of ​​the eagle; No. The hunting area of ​​the Skyhawk at the first iteration; express A random value between ; express A random value between ; represents the global impact factor; Indicates The optimal flight position of the Skyhawk at the iteration; represents the local impact factor; represents the fifth constant; Indicates The average flight position of the Skyhawk at the iteration; represents the sixth constant; represents the upper bound of the eagle population; represents the lower bound of the eagle population; represents a parameter that decreases linearly with the number of iterations; represents the seventh constant; represents the annealing probability; represents the exponential function; Represents the fitness function.

[0034] Optional, when and When , the Skyhawk is in the shrinking development stage. In the shrinking development stage, the Skyhawk's attack position is updated. The formula is:

[0035] ;

[0036] ;

[0037] In the formula, Indicates the current iteration number; Indicates the maximum number of iterations; Indicates that it is located A random value between ; Indicates The attack position of the Skyhawk in the first iteration; Indicates The mass function of the Eagle at the iteration; Indicates The optimal flight position of the Skyhawk at the iteration; represents the motion parameters of a skyhawk as it monitors its prey during flight; Indicates The initial flight position of the Skyhawk at the iteration; It represents the flight slope of the eagle as it follows its prey during flight; Indicates that the Eagle is Flight distribution function in the spatial dimension.

[0038] Optionally, the preset target prey function formula is:

[0039] ;

[0040] In the formula, Represents an infinite value; Indicates time; Represents the input and output error signal of the permanent magnet synchronous motor.

[0041] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm described in the first aspect are implemented.

[0042] In a third aspect, the present invention provides a computer system, comprising:

[0043] Memory, for storing computer instructions;

[0044] A processor is used to execute the computer instructions to implement the steps of the permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm described in the first aspect.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention overcomes the difficulty of adjusting controller parameters in the permanent magnet synchronous motor speed control model by introducing an improved Sky Eagle optimization algorithm, which achieves the optimal configuration of global search and local search capabilities by virtue of its fast convergence, strong global search capability and high solution accuracy. In the parameter optimization process, the dynamic simplex method strategy and annealing simulation-golden sine update strategy are used to expand the selection range. The algorithm exhibits excellent performance and significantly reduces the labor intensity of scientific researchers in adjusting parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 FIG. 1 is a flow chart of a permanent magnet synchronous motor speed control method based on an improved Sky Eagle algorithm in an embodiment of the present invention;

[0048] Figure 2 The figure is a schematic diagram of the flow of the improved Sky Eagle algorithm of the present invention in one embodiment;

[0049] Figure 3 The figure shows a schematic diagram of the structure of a speed control model of a permanent magnet synchronous motor in one embodiment of the present invention;

[0050] Figure 4 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F1 in one embodiment;

[0051] Figure 5 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F2 in one embodiment;

[0052] Figure 6 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F3 in one embodiment;

[0053] Figure 7 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F4 in one embodiment;

[0054] Figure 8The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F5 in one embodiment;

[0055] Fig. 9 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F6 in one embodiment;

[0056] Fig.10 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F7 in one embodiment;

[0057] Fig.11 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F8 in one embodiment;

[0058] Fig.12 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F9 in one embodiment;

[0059] Fig.13 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F10 in one embodiment;

[0060] Fig.14 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F11 in one embodiment;

[0061] Fig.15 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F12 in one embodiment;

[0062] Fig.16 The figure shows a comparison of the convergence curves of the improved Sky Eagle algorithm of the present invention and the existing algorithm for the test function F13 in one embodiment;

[0063] Fig.17 The figure shows a comparison diagram of the speed error of the permanent magnet synchronous motor speed regulation control in one embodiment of the improved Sky Eagle algorithm of the present invention and the Sky Eagle algorithm. DETAILED DESCRIPTION

[0064] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0065] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0066] Example 1

[0067] like Figure 1 As shown, this embodiment introduces a permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm, which specifically includes the following steps:

[0068] Get permanent magnet synchronous motor data.

[0069] By inputting the permanent magnet synchronous motor data into a pre-trained permanent magnet synchronous motor speed control model, a speed regulation result of the permanent magnet synchronous motor can be output, and the result is a speed regulation result of the permanent magnet synchronous motor.

[0070] The synchronous motor speed control model is trained using the improved Sky Eagle algorithm. The training process is as follows: Figure 2 As shown, specifically:

[0071] The eagle population is obtained, and the initial flight position of each eagle in the eagle population is calculated. The formula is:

[0072] ;

[0073] In the formula, Indicates The initial flight position of the Skyhawk at the iteration; represents the lower bound of the eagle population; Indicates that it is located A random value between ; Represents the upper bound of the eagle population.

[0074] The improved Skyhawk algorithm divides the Skyhawk population into an expansion exploration phase, a contraction exploration phase, an expansion development phase and a contraction development phase.

[0075] In the expansion exploration phase, the initial flight position of the Skyhawk is used to update the flight position of the Skyhawk. In the reduction exploration phase, the flight area of ​​the Skyhawk is updated. In the expansion development phase, the hunting area of ​​the Skyhawk is updated. In the reduction development phase, the initial flight position of the Skyhawk is used to update the attack position of the Skyhawk.

[0076] In the expansion and exploration stage, this embodiment uses the dynamic simplex method strategy to expand the optimal range of the Eagle's flight position, generate a flight position of the Eagle with better quality, which is helpful to obtain a better search area in the next stage and effectively improve the search speed of the algorithm; in the expansion and development stage, the annealing simulation-golden sine update strategy is used to expand the optimal range of the Eagle's predation area, making the algorithm's optimization range more comprehensive and avoiding the defect of excessively seeking the optimal solution and falling into the local optimum.

[0077] In this embodiment, when and When the Eagle was in the stage of expansion and exploration; and When the Eagle is in the shrinking exploration phase; when and At that time, Tianying was in the stage of expansion and development; and At that time, the Skyhawk was in the stage of shrinking development; Indicates the current iteration number; Indicates the maximum number of iterations; Indicates that it is located A random value between .

[0078] In a specific embodiment, when and When , the Eagle is in the expansion exploration stage. In the expansion exploration stage, the Eagle's initial flight position is used to update the Eagle's flight position. The dynamic simplex method strategy is used to optimize the individual flight positions of each Eagle in the Eagle population. The purpose of expanding the search range of the algorithm is to enhance the search for high-quality solutions and provide a better population for the next iteration. The formula is:

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] In the formula, Indicates In the first iteration, the dynamic simplex method strategy is used to update the flight position of the eagle; represents the upper bound of Tianying in the dim-th spatial dimension; Indicates the lower bound of Tianying in the dim-th spatial dimension; represents the dynamic scaling factor; Indicates The flight position of the Skyhawk at the iteration; Indicates The optimal flight position of the Skyhawk at the iteration; Indicates The average flight position of the Skyhawk at the iteration; Indicates the number of Skyhawks.

[0084] In a second specific embodiment, when and When , the Eagle is in the shrinking exploration stage. In the shrinking exploration stage, the Eagle's flight area is updated, the area selected by the target prey function is carefully searched, the optimization interval is converged, and preparations are made for optimization. The formula is:

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] In the formula, Indicates The flight area of ​​the Skyhawk during the first iteration; Indicates that the Eagle is The flight distribution function of the problem dimension, Represents the dimension of the problem; Indicates The flight position of a hawk randomly selected from the hawk population in the first iteration; represents the first intermediate parameter; represents the second intermediate parameter; and are two parameters that describe the shape of the spiral during the search; Represents the first constant. In this embodiment =0.01; Indicates that it is located A random value between ; Represents the flight dynamics parameter, which is used to measure the upper bound of the search area; Indicates that it is located A random value between ; represents the gamma function; Represents the second constant. In this embodiment =1.5; Indicates the search step length; Indicates the search angle; Represents the number of search operations, A random value between ; Represents the third constant. In this embodiment =0.00565; express Integer in ; Represents the fourth constant. In this embodiment =0.005; express In this embodiment, =1.5π.

[0090] In a third specific embodiment, when and When , the Eagle is in the expansion and development stage. In the expansion and development stage, the Eagle's prey area is updated. The Eagle's optimization method will continue to approach the interval where the optimal solution is located after convergence. The annealing simulation-golden sine update strategy is used to optimize the Eagle's prey position, making the algorithm more fully optimized in the search space, helping the Eagle to find new food in the local search stage, and avoiding the defect of excessively seeking the optimal solution and falling into the local optimum. The formula is:

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] In the formula, Indicates In the second iteration, the annealing simulation-golden sine strategy is used to update the hunting area of ​​the eagle; No. The hunting area of ​​the Skyhawk at the first iteration; express A random value between ; express A random value between ; and Determine the moving distance and direction of the Sky Eagle in the next iteration respectively; represents the global impact factor; represents the local impact factor; Represents the fifth constant. In this embodiment =0.1; Represents the sixth constant. In this embodiment =0.1; represents the upper bound of the eagle population; represents the lower bound of the eagle population; represents a parameter that decreases linearly with the number of iterations; represents the seventh constant; represents the annealing probability; represents the exponential function; Represents the fitness function.

[0096] In this embodiment, the annealing probability, simulated annealing accepts the worse solution with a certain probability, and adds a judgment mechanism. The fitness value is greater than If the fitness value is , then accept the fitness after the annealing simulation-golden sine strategy update; otherwise, accept the fitness before the update with a certain annealing probability P.

[0097] In a fourth specific embodiment, when and When , the Eagle is in the shrinking development stage. When updating the Eagle's attack position in the shrinking development stage, the optimal solution of the convergence interval will be searched. The formula is:

[0098] ;

[0099] ;

[0100] In the formula, Indicates The attack position of the Skyhawk in the first iteration; Indicates The mass function of the Eagle at the iteration; represents the motion parameters of the eagle when monitoring its prey during flight, A random number between It represents the flight slope of the Skyhawk as it follows its prey during flight, decreasing from 2 to 0; Indicates that the Eagle is Flight distribution function in the spatial dimension.

[0101] According to the preset target prey function, the optimal flight position and attack position of the Skyhawk are obtained by using the untrained permanent magnet synchronous motor speed control model, such as Figure 3 The figure shows the structure diagram of the permanent magnet synchronous motor speed control model. The model parameters include: controller bandwidth w c , disturbance compensation gain b0, observer bandwidth w0, disturbance gain b, proportional coefficient kp, integral coefficient ki, use the improved sky algorithm to iteratively optimize and obtain the optimal model parameters. The preset target prey function formula is:

[0102] ;

[0103] In the formula, Represents an infinite value; Indicates time; Represents the input and output error signal of the permanent magnet synchronous motor.

[0104] The parameters representing the flight position and attack position of the optimal Skyhawk are used as the parameters kp, ki, b, w0, w c and b0.

[0105] Example 2

[0106] like Figure 4-16 As shown, this embodiment is a performance test simulation performed on the improved Sky Eagle optimization algorithm of the present invention.

[0107] As shown in Table 1, 13 standard test functions were carefully selected, and the performance of the original Eagle algorithm (AO) and the improved Eagle algorithm (EAO) of the present invention were tested with the help of the MATLAB platform. At the same time, we also conducted a comparative study with the Grey Wolf Optimization Algorithm (GWO) and the Seahorse Optimization Algorithm (SHO). Among them, the F1~F7 functions are unimodal types, while the F8~F13 functions are multimodal types. The unimodal test function is an ideal choice for evaluating the search efficiency and convergence speed of the optimization algorithm because it only contains one global optimal value and no local optimal solution. On the contrary, the multimodal test function becomes an effective tool for testing the global search ability of the algorithm because the number of its local optimal solutions increases sharply with the increase of dimension.

[0108] Table 1 Test functions

[0109]

[0110] Under the premise of keeping the parameter settings consistent (setting the population size to a constant value of 50 and setting the maximum number of iterations to less than 500 times), we used the MATLAB platform to independently perform 50 experiments on each test function for the four optimization algorithms and conducted a systematic statistical analysis of the experimental results. Table 2 summarizes in detail the three core performance indicators of the optimal solution, average value, and standard deviation of these algorithms when solving 13 test functions. In addition, Figures 4 to 16 The convergence curves of the algorithms in the process of pursuing the global optimal solution of the 13 test functions are presented in an intuitive way. After this series of in-depth comparative analysis, it is strongly proved that the improved Sky Eagle algorithm of the present invention has more excellent performance in terms of global search capability, convergence rate and solution accuracy.

[0111] Table 2 Experimental results data

[0112]

[0113] In this embodiment, we adopt the permanent magnet synchronous motor speed control method based on the improved Skyhawk algorithm described in Example 1. The parameter set corresponding to the optimal Skyhawk position finally determined by the optimization process (specifically including kp, ki, b, w0, wc and b0) are applied to the controller of this control scheme. The structural diagram of this control scheme is shown in Figure 3 . Fig.17 The speed error output result under this control scheme is intuitively displayed. Through comparative analysis, we found that the parameter configuration obtained by the improved Sky Eagle algorithm proposed in this embodiment can effectively enhance the speed control performance of the motor speed regulation, significantly reduce the error between the input command and the output feedback, and thus achieve higher control accuracy.

[0114] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0115] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A permanent magnet synchronous motor speed control method based on improved Sky Eagle algorithm, characterized in that: include: Get permanent magnet synchronous motor data; Inputting the permanent magnet synchronous motor data into a pre-trained permanent magnet synchronous motor speed control model, and outputting a speed control result of the permanent magnet synchronous motor; The permanent magnet synchronous motor speed control model is trained using the improved Sky Eagle algorithm including: Acquire a population of eagles, and calculate an initial flight position of each eagle in the population of eagles; In the expansion exploration phase, the initial flight position of the Eagle is used to update the flight position of the Eagle. In the reduction exploration phase, the flight area of ​​the Eagle is updated. In the expansion development phase, the Eagle's predation area is updated. In the reduction development phase, the initial flight position of the Eagle is used to update the attack position of the Eagle. In the expansion exploration phase, the dynamic simplex method strategy is used to expand the optimal range of the Eagle's flight position. In the expansion development phase, the annealing simulation-golden sine update strategy is used to expand the optimal range of the Eagle's predation area. According to the preset target prey function, the untrained permanent magnet synchronous motor speed control model is used to obtain the optimal flight position and attack position of the Skyhawk; the parameters represented by the flight position and attack position of the optimal Skyhawk are used as the parameters of the permanent magnet synchronous motor speed control model.

2. The permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm according to claim 1 is characterized in that: The calculation formula of the initial flight position of each eagle in the eagle population is as follows: ； In the formula, Indicates The initial flight position of the Skyhawk at the iteration; represents the lower bound of the eagle population; Indicates that it is located A random value between ; Represents the upper bound of the eagle population.

3. The permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm according to claim 2 is characterized in that: when and When , the Eagle is in the expansion exploration stage. In the expansion exploration stage, the Eagle's initial flight position is used to update the Eagle's flight position. The formula is: ； ； ； ； In the formula, Indicates In the first iteration, the dynamic simplex method strategy is used to update the flight position of the eagle; represents the upper bound of Tianying in the dim-th spatial dimension; Indicates the lower bound of Tianying in the dim-th spatial dimension; represents the dynamic scaling factor; Indicates The flight position of the Skyhawk at the iteration; Indicates the current iteration number; Indicates the maximum number of iterations; Indicates The optimal flight position of the Skyhawk at the iteration; Indicates The average flight position of the Skyhawk at the iteration; Indicates the number of Skyhawks.

4. The permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm according to claim 1 is characterized in that: when and When , the Eagle is in the shrinking exploration phase. In the shrinking exploration phase, the Eagle's flight area is updated. The formula is: ； ； ； ； In the formula, Indicates the current iteration number; Indicates the maximum number of iterations; Indicates that it is located A random value between ; Indicates The flight area of ​​the Skyhawk during the first iteration; Indicates The optimal flight position of the Skyhawk at the iteration; Indicates that the Eagle is Flight distribution function of the problem dimension; Indicates The flight position of a hawk randomly selected from the hawk population in the first iteration; represents the first intermediate parameter; represents the second intermediate parameter; represents the first constant; Indicates that it is located A random value between ; Indicates flight dynamic parameters; Indicates that it is located A random value between ; represents the gamma function; represents the second constant; Indicates the search step length; Indicates the search angle; Indicates the number of search operations; represents the third constant; express Integers in Represents spatial dimension; represents the fourth constant; express A random value between .

5. The permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm according to claim 1 is characterized in that: when and When , the Eagle is in the expansion and development stage. In the expansion and development stage, the Eagle's prey area is updated. The formula is: ； ； ； ； In the formula, Indicates the current iteration number; Indicates the maximum number of iterations; Indicates that it is located A random value between ; Indicates In the second iteration, the annealing simulation-golden sine strategy is used to update the hunting area of ​​the eagle; No. The hunting area of ​​the Skyhawk at the first iteration; express A random value between ; express A random value between ; represents the global impact factor; Indicates The optimal flight position of the Skyhawk at the iteration; represents the local impact factor; represents the fifth constant; Indicates The average flight position of the Skyhawk at the iteration; represents the sixth constant; represents the upper bound of the eagle population; represents the lower bound of the eagle population; represents a parameter that decreases linearly with the number of iterations; represents the seventh constant; represents the annealing probability; represents the exponential function; Represents the fitness function.

6. The permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm according to claim 1 is characterized in that: when and When , the Skyhawk is in the shrinking development stage. In the shrinking development stage, the Skyhawk's attack position is updated. The formula is: ； ； In the formula, Indicates the current iteration number; Indicates the maximum number of iterations; Indicates that it is located A random value between ; Indicates The attack position of the Skyhawk in the first iteration; Indicates The mass function of the Eagle at the iteration; Indicates The optimal flight position of the Skyhawk at the iteration; represents the motion parameters of a skyhawk as it monitors its prey during flight; Indicates The initial flight position of the Skyhawk at the iteration; It represents the flight slope of the eagle as it follows its prey during flight; Indicates that the Eagle is Flight distribution function in the spatial dimension.

7. The permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm according to claim 1 is characterized in that: The preset target prey function formula is: ； In the formula, Represents an infinite value; Indicates time; Represents the input and output error signal of the permanent magnet synchronous motor.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm described in any one of claims 1 to 7 are implemented.

9. A computer system, characterized in that: include: Memory, for storing computer instructions; A processor is used to execute the computer instructions to implement the steps of the permanent magnet synchronous motor speed control method based on the improved Sky Eagle algorithm described in any one of claims 1 to 7.

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