A control optimization method for permanent magnet coreless motor of refrigeration unit compressor

Abstract

The invention discloses a control optimization method for a permanent magnet coreless motor of a refrigeration unit compressor, which belongs to the technical field of PID control. The method comprises the following steps: S1, constructing a simulation control system for a permanent magnet coreless motor of a refrigeration unit compressor; S2, improving an ivy algorithm; S3, using the improved ivy algorithm to adjust parameters of a PID controller of a speed loop and a current loop, and obtaining the best three parameters Kp, Ki and Kd by optimizing the algorithm; S4, inputting the three best PID parameters adjusted by the ivy algorithm into the simulation control system for a permanent magnet coreless motor of a refrigeration unit compressor, and optimizing the control effect of the permanent magnet coreless motor; and optimizing the PID controller by using the improved ivy algorithm to improve the stability and robustness of the PID control.

Description

Technical Field

[0001] The invention belongs to the field of PID control optimization, and in particular relates to a control optimization method for a permanent magnet coreless motor of a refrigeration unit compressor. Background Art

[0002] The compressor of the refrigeration unit is the core component of the refrigeration system. Its performance will directly affect the refrigeration effect and energy efficiency of the system. The permanent magnet coreless motor (PMCM) has the advantages of high efficiency, low loss, and no core harmonics. It is suitable for high-efficiency and low-noise compressor drive. By optimizing the control of the motor, the performance and reliability of the refrigeration system can be significantly improved. During the operation of the motor, the speed and current need to be controlled by dual closed-loop control. The speed loop and current loop are generally controlled by a PID controller to achieve stable operation of the motor.

[0003] PID controller consists of three parts: proportional control, integral control, and differential control. It is a classic feedback control algorithm and is widely used in various industrial control systems, including temperature control, position control, speed control, etc. PID controller adjusts the control quantity so that the system output follows the expected value, thereby achieving precise control. For a general PID controller, the most important thing is to set the parameters Kp, Ki, and Kd. The setting of parameters requires a lot of experience and experiments, and is inefficient. In addition, it has poor control effects on complex nonlinear systems. In order to solve such problems, the PID parameters can be optimized through intelligent optimization algorithms.

[0004] The Ivy Algorithm (IVYA) is derived from the growth pattern of the ivy plant. It simulates the coordinated and orderly population growth as well as the spread and evolution of the ivy plant. It uses differential equations and data-intensive experimental processes to model the growth rate of the ivy plant. IVYA has excellent performance in solving optimization problems and can improve the performance and efficiency of PID control. However, like common intelligent optimization algorithms, the Ivy Algorithm has poor adaptability, slow response speed and falls into local optimal solutions in solving multi-dimensional problems. This will greatly affect the optimization effect of the Ivy Algorithm on the PID controller. Summary of the invention

[0005] The purpose of the present invention is to improve the global search capability of the standard ivy algorithm and the optimization accuracy of the algorithm by improving the ivy algorithm. When the algorithm encounters a situation where it is trapped in a local optimal solution during the optimization process, it can quickly jump out of the local optimal solution and continue to optimize. In addition, the three parameters Kp, Ki and Kd of the PID controller of the speed loop and current loop of the permanent magnet coreless motor of the compressor are optimized by improving the ivy algorithm, so as to improve the setting speed of the PID controller parameters, reduce the workload of manual tuning, and improve the robustness and adaptability of the permanent magnet coreless motor control of the compressor.

[0006] To achieve the above-mentioned purpose, the present invention adopts a control optimization method for a permanent magnet coreless motor of a refrigeration unit compressor, and the specific steps of the method are as follows.

[0007] S1. Construct a permanent magnet coreless motor simulation control system for a refrigeration unit compressor, wherein the control system includes an error calculation module, a speed loop PID controller module, a current loop PID controller module, an improved ivy algorithm module, a permanent magnet coreless motor module, a current detection module, and a speed detection module.

[0008] S2. Improve the Ivy algorithm. The specific improvement strategy is:

[0009] S21. A reverse learning strategy integrating segmented cross mapping is proposed to improve the mathematical model of the extended growth of the Ivy algorithm. According to the idea of ​​segmented mapping, two different mappings are integrated to form a cross mapping, and the current population position is reversely calculated to obtain the reverse position. The position with better fitness value between the forward position and the reverse position is selected as the update position.

[0010] S22. An adaptive balanced search strategy is proposed to improve the upward growth mathematical model of the Ivy algorithm. The influence weight of the optimal solution on the updated position is adjusted by the nonlinear change of the adaptive factor β, and the position update of the search agent is balanced by the probability P.

[0011] S3. Use the improved ivy algorithm to adjust the parameters of the PID controller of the speed loop and the current loop, and obtain the optimal three parameters Kp, Ki, and Kd through algorithm optimization.

[0012] S4. Input the three optimal PID parameters adjusted by the ivy algorithm into the permanent magnet coreless motor simulation control system of the refrigeration unit compressor to optimize the control effect of the permanent magnet coreless motor.

[0013] Preferably, in the permanent magnet coreless motor simulation control system of the refrigerator compressor described in S1, the desired speed is first input into the control system as an input signal, the actual speed detected by the speed detection module and the desired speed are transmitted to the error calculation module together, the obtained error e1(t) is transmitted to the speed loop PID control module, and the parameters are adjusted by the improved ivy algorithm, and the control quantity u1(t) and the actual current detected by the current detection module are transmitted to the error calculation module together, the error e2(t) is transmitted to the current loop PID control module, and the parameters are adjusted by the improved ivy algorithm, and the control quantity u2(t) is transmitted to the permanent magnet coreless motor module for speed control.

[0014] Optimally, in S21, a reverse learning strategy integrating segmented cross mapping is to divide the mapping into two parts according to the idea of ​​segmented mapping, one part integrates Fourier chaos mapping and the other part integrates logistic mapping, converts the mapping value to the dimension size of the problem, performs reverse learning calculation on the current population position, and obtains the reverse position With original position Compare the fitness values ​​and select the optimal fitness value as the update position through the greedy strategy. The improved expansion growth mathematical model is shown in formula (1):

[0015] (1);

[0016] In formula (1), Indicates the location of the updated search agent, Indicates the location of the current search agent. The segmented cross-mapping value is shown in formula (3), dim represents the problem dimension of the algorithm, represents the optimal position at the current stage, Gv represents the ivy growth rate as shown in formula (2);

[0017] (2);

[0018] In formula (2), represents the current position of the search agent, ub represents the upper limit of the search space, lb represents the lower limit of the search space, Iter represents the number of iterations, dim represents the problem dimension, N(1,dim) represents the vector of dim dimension, and the value is a standard normal distribution random number, rand represents a random number between [0,1];

[0019] (3);

[0020] In formula (3), represents the updated segment cross-mapping value, r represents the control parameter, and its value is between [3.57, 4];

[0021] The reverse solution for the current position is , replace the search agent position in formula (1) Replace with You can get the reverse update position .

[0022] Preferably, by improving a reverse learning strategy that integrates segmented cross-mapping, the advantages of the two mappings can be better combined, enhancing the algorithm's global search capability and ability to quickly jump out of local optimal solutions during the optimization process. Reverse learning can increase the diversity of the algorithm and improve the algorithm's optimization capability. By improving the optimization of the ivy algorithm, the control accuracy and response speed of the PID controller are improved, and the control effect of the motor is better optimized.

[0023] Preferably, in S22, an adaptive balanced search strategy is to change the adaptive factor β nonlinearly with different iteration stages, thereby affecting the update of the search agent. The upward growth mathematical model improved by the adaptive balanced search strategy is shown in formula (4):

[0024] (4);

[0025] In formula (4), Indicates the location of the updated search agent, represents the optimal position at the current stage, β is the adaptive factor as shown in formula (5), p represents the update probability, which is a random number between [0,1], dim is the problem dimension, and Gv represents the ivy growth rate as shown in formula (2);

[0026] (5);

[0027] In formula (5), α indicates that the initial impact factor is 0.5, iter indicates the current iteration number, and maxiter indicates the maximum iteration number.

[0028] Preferably, the algorithm's adaptability is increased by improving the upward growth mathematical model through an adaptive balanced search strategy. In the early stages of the algorithm iteration, the value of the adaptive factor is relatively large, which helps the algorithm to conduct a wide search and explore different areas in the solution space. In the later stages of the algorithm iteration, the value of the adaptive factor gradually decreases, which helps the algorithm to shrink more finely and gradually approach the optimal solution, thereby increasing the algorithm's local search capability.

[0029] Preferably, in S3, the parameters of the PID controllers of the speed loop and the current loop are adjusted using the improved ivy algorithm, and the specific steps of obtaining the optimal three parameters Kp, Ki, and Kd by optimizing the algorithm are as follows:

[0030] S31, encoding the parameters Kp, Ki, Kd of the PID controller into an Ivy algorithm search space solution;

[0031] S32, initialize the parameters of the improved Ivy algorithm, including the algorithm's population size N, problem dimension dim, maximum number of iterations maxiter, search space upper limit ub, and contraction space lower limit lb;

[0032] S33, initialize the population, calculate the fitness values ​​of the individuals in the Ivy Algorithm population, sort them according to the size of the fitness values, and determine the best individual in the initial population. The calculation formula of the fitness value is:

[0033] (6);

[0034] In formula (6), exp(t) represents the expected output value at time t, act(t) represents the actual output value at time t, T represents the total time of system operation, and J represents the fitness value;

[0035] S34, updating the population position by improving the mathematical model of the Ivy algorithm, and searching for individuals with the best fitness value through the population update;

[0036] S35. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to search for the best solution. If so, exit the optimization iteration and use the output optimal solution as the optimal Kp, Ki, and Kd parameter values ​​of the PID controller. Input the parameter values ​​into the permanent magnet coreless motor simulation control system of the refrigeration unit compressor to complete the control of the system.

[0037] Preferably, the specific steps of updating the population position by improving the mathematical model of the ivy algorithm described in S34 are:

[0038] Step 1: Calculate the growth rate Gv of individuals in the population. The calculation formula is shown in formula (2). Calculate the random parameter φ. , rand is a random number between [0,1];

[0039] Step 2: If the fitness value of the current position is less than φ multiplied by the fitness value of the best position of the current iteration, the improved extended growth mathematical model is executed to update the population position. The specific formula is shown in formula (1);

[0040] Step 3: If the fitness value of the current position is greater than or equal to φ multiplied by the fitness value of the best position of the current iteration, the improved upward growth mathematical model is executed to update the population position. The specific formula is shown in formula (4);

[0041] Step 4: Evaluate the fitness value of the new position, update the best position of the population, and proceed to the next iteration.

[0042] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: by improving the ivy algorithm, the algorithm's adaptability and global search capabilities in the optimization process are increased, and when the algorithm falls into a local optimal solution, it can quickly jump out and continue to optimize, thereby improving the algorithm's optimization accuracy and speed. By using the improved ivy algorithm in the permanent magnet coreless motor control system of the refrigeration unit compressor to optimize the motor's speed loop and current loop, the system's adaptability and robustness are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flow chart of a control optimization method of a permanent magnet coreless motor for a refrigeration unit compressor.

[0044] Figure 2 This is the model diagram of the simulation control system of the permanent magnet coreless motor of the refrigeration unit compressor.

[0045] Figure 3 Comparison chart of the control effect of the improved Ivy algorithm and the standard Ivy algorithm on the PID controller.

[0046] Figure 4 Comparison chart of fitness values ​​between the improved Ivy algorithm and the standard Ivy algorithm in the optimization process. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] The present invention provides a technical solution: a control optimization method for a permanent magnet coreless motor of a refrigeration unit compressor, specifically comprising the following steps: Figure 1 shown.

[0049] S1. Construct a permanent magnet coreless motor simulation control system for a refrigeration unit compressor. The control system includes an error calculation module, a speed loop PID controller module, a current loop PID controller module, an improved ivy algorithm module, a permanent magnet coreless motor module, a current detection module, and a speed detection module. Figure 2 shown.

[0050] Furthermore, in the permanent magnet coreless motor simulation control system of the refrigerator compressor described in S1, the desired speed is first input into the control system as an input signal, the actual speed detected by the speed detection module and the desired speed are transmitted to the error calculation module together, the obtained error e1(t) is transmitted to the speed loop PID control module, and the parameters are adjusted by the improved ivy algorithm, and the control quantity u1(t) and the actual current detected by the current detection module are transmitted to the error calculation module together, the error e2(t) is transmitted to the current loop PID control module, and the parameters are adjusted by the improved ivy algorithm, and the control quantity u2(t) is transmitted to the permanent magnet coreless motor module for speed control.

[0051] S2. Improve the Ivy algorithm. The specific improvement strategy is:

[0052] S21. A reverse learning strategy integrating segmented cross mapping is proposed to improve the mathematical model of the extended growth of the Ivy algorithm. According to the idea of ​​segmented mapping, two different mappings are integrated to form a cross mapping, and the current population position is reversely calculated to obtain the reverse position. The position with better fitness value between the forward position and the reverse position is selected as the update position.

[0053] S22. An adaptive balanced search strategy is proposed to improve the upward growth mathematical model of the Ivy algorithm. The influence weight of the optimal solution on the updated position is adjusted by the nonlinear change of the adaptive factor β, and the position update of the search agent is balanced by the probability P.

[0054] Furthermore, in S21, a reverse learning strategy integrating segmented cross mapping is to divide the mapping into two parts according to the idea of ​​segmented mapping, one part integrating Fourier chaos mapping and the other part integrating logistic mapping, converting the mapping value to the dimension size of the problem, performing reverse learning calculation on the current population position, and obtaining the reverse position With original position Compare the fitness values ​​and select the optimal fitness value as the update position through a greedy strategy. By improving a reverse learning strategy that integrates segmented cross-mapping, the advantages of the two mappings can be better combined, and the algorithm's global search ability and ability to quickly jump out of the local optimal solution in the optimization process can be enhanced. Reverse learning can increase the diversity of the algorithm and improve the algorithm's optimization ability. The improved extended growth mathematical model is shown in formula (1):

[0055] (1);

[0056] In formula (1), Indicates the location of the updated search agent, Indicates the location of the current search agent. The segmented cross-mapping value is shown in formula (3), dim represents the problem dimension of the algorithm, represents the optimal position at the current stage, Gv represents the ivy growth rate as shown in formula (2);

[0057] (2);

[0058] In formula (2), represents the current position of the search agent, ub represents the upper limit of the search space, lb represents the lower limit of the search space, Iter represents the number of iterations, dim represents the problem dimension, N(1,dim) represents the vector of dim dimension, and the value is a standard normal distribution random number, rand represents a random number between [0,1];

[0059] (3);

[0060] In formula (3), represents the updated segment cross-mapping value, r represents the control parameter, and its value is between [3.57, 4];

[0061] The reverse solution for the current position is , replace the search agent position in formula (1) Replace with You can get the reverse update position .

[0062] Furthermore, in S22, an adaptive balanced search strategy is to change the adaptive factor β nonlinearly with different iteration stages, thereby affecting the update of the search agent. The adaptive balanced search strategy improves the upward growth mathematical model and increases the adaptive ability of the algorithm. In the early stage of algorithm iteration, the value of the adaptive factor is large, which helps the algorithm to conduct a wide search and explore different areas in the solution space. In the later stage of algorithm iteration, the value of the adaptive factor gradually decreases, which helps the algorithm to shrink more finely and gradually approach the optimal solution, thereby increasing the local search ability of the algorithm. The upward growth mathematical model improved by the adaptive balanced search strategy is shown in formula (4):

[0063] (4);

[0064] In formula (4), Indicates the location of the updated search agent, represents the optimal position at the current stage, β is the adaptive factor as shown in formula (5), p represents the update probability, which is a random number between [0,1], dim is the problem dimension, and Gv represents the ivy growth rate as shown in formula (2);

[0065] (5);

[0066] In formula (5), α indicates that the initial impact factor is 0.5, iter indicates the current iteration number, and maxiter indicates the maximum iteration number.

[0067] S3. Use the improved ivy algorithm to adjust the parameters of the PID controller of the speed loop and the current loop, and obtain the best three parameters Kp, Ki, and Kd through the algorithm optimization. The specific steps are:

[0068] S31, encoding the parameters Kp, Ki, Kd of the PID controller into an Ivy algorithm search space solution;

[0069] S32, initialize the parameters of the improved Ivy algorithm, including the algorithm's population size N, problem dimension dim, maximum number of iterations maxiter, search space upper limit ub, and contraction space lower limit lb;

[0070] S33, initialize the population, calculate the fitness values ​​of the individuals in the Ivy Algorithm population, sort them according to the size of the fitness values, and determine the best individual in the initial population. The calculation formula of the fitness value is:

[0071] (6);

[0072] In formula (6), exp(t) represents the expected output value at time t, act(t) represents the actual output value at time t, T represents the total time of system operation, and J represents the fitness value;

[0073] S34, updating the population position by improving the mathematical model of the Ivy algorithm, and searching for individuals with the best fitness value through the population update;

[0074] S35. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to search for the best solution. If so, exit the optimization iteration and use the output optimal solution as the optimal Kp, Ki, and Kd parameter values ​​of the PID controller. Input the parameter values ​​into the permanent magnet coreless motor simulation control system of the refrigeration unit compressor to complete the control of the system.

[0075] Furthermore, the specific steps of updating the population position by improving the mathematical model of the Ivy algorithm described in S34 are:

[0076] Step 1: Calculate the growth rate Gv of individuals in the population. The calculation formula is shown in formula (2). Calculate the random parameter φ. , rand is a random number between [0,1];

[0077] Step 2: If the fitness value of the current position is less than φ multiplied by the fitness value of the best position of the current iteration, the improved extended growth mathematical model is executed to update the population position. The specific formula is shown in formula (1);

[0078] Step 3: If the fitness value of the current position is greater than or equal to φ multiplied by the fitness value of the best position of the current iteration, the improved upward growth mathematical model is executed to update the population position. The specific formula is shown in formula (4);

[0079] Step 4: Evaluate the fitness value of the new position, update the best position of the population, and proceed to the next iteration.

[0080] S4. Input the three optimal PID parameters adjusted by the ivy algorithm into the permanent magnet coreless motor simulation control system of the refrigeration unit compressor to optimize the control effect of the permanent magnet coreless motor.

[0081] In order to verify that the performance of the permanent magnet coreless motor control optimization method for a refrigeration unit compressor proposed in the present invention is stronger than other methods, the present invention will perform simulations through Matlab software and Simulink software, and compare the improved ivy algorithm optimized PID control and the standard ivy algorithm optimized PID control. First, the mathematical model of the ivy algorithm is improved by Matlab, and the permanent magnet coreless motor simulation control system for the refrigeration unit compressor is constructed by Simulink. The parameters of the improved ivy algorithm are set, including the population size N=100, the problem dimension dim=3, and the maximum number of iterations is 20. In the permanent magnet coreless motor simulation control system for the refrigeration unit compressor, the performance of the PID controller determines the control performance of the motor speed loop and the current loop, and the optimization performance of the algorithm affects the response speed and overshoot of the PID controller.

[0082] Figure 3 The control effect comparison chart of the improved ivy algorithm and the standard ivy algorithm on the PID controller is shown in the figure. The target value control amount is set to 1 unit. It can be seen from the figure that the overshoot of the control amount of the PID controller optimized by the improved ivy algorithm is lower, the time to reach a stable state is shorter, and the oscillation during the tuning process is smaller, which can achieve better control effect.

[0083] Figure 4 A comparison chart of the fitness values ​​of the optimization process of the improved Ivy algorithm and the standard Ivy algorithm is provided. According to the principle that the smaller the fitness value, the better the individual position, it can be seen from the figure that the improved Ivy algorithm can reach the optimal value faster in the optimization process and performs better in optimization accuracy, which improves the response speed and stability of the permanent magnet coreless motor control system of the refrigeration unit compressor and increases the control accuracy and performance of the system.

Claims

1. A method for optimizing control of a permanent magnet coreless motor of a refrigeration unit compressor, characterized in that: The specific steps are: S1. Construct a permanent magnet coreless motor simulation control system for a refrigeration unit compressor, wherein the control system includes an error calculation module, a speed loop PID controller module, a current loop PID controller module, an improved ivy algorithm module, a permanent magnet coreless motor module, a current detection module, and a speed detection module; S2. Improve the Ivy algorithm. The specific improvement strategy is: S21. A reverse learning strategy integrating segmented cross mapping is proposed to improve the mathematical model of expansion growth of the Ivy algorithm. According to the idea of ​​segmented mapping, two different mappings are integrated to form a cross mapping. The current population position is reversely calculated to obtain the reverse position. The position with better fitness value in the forward position and the reverse position is selected as the update position. The improved mathematical model of expansion growth is shown in formula (1): I new =I+|X n |·dim·(I-I best )+X n ·dim·Gv (1); In formula (1), I new represents the updated position of the search agent, I represents the current position of the search agent, Gv represents the ivy growth rate as shown in formula (2), X n The segmented cross-mapping value is shown in formula (3), dim represents the problem dimension of the algorithm, and I best Indicates the optimal position at the current stage; In formula (2), I represents the position of the current search agent, ub represents the upper limit of the search space, and l b Iter is the number of iterations, dim is the problem dimension, N(1, dim) is a vector of dim dimension, and its value is a standard normal distribution random number. rand is a random number between [0, 1]. In formula (3), X n+1 represents the updated segment cross-mapping value, r represents the control parameter, and its value is between [3.57, 4]; The reverse solution of the current position is I′=ub+lb-I. Replacing the search agent position I in formula (1) with i′ can obtain the reverse updated position I′. new ; S22. An adaptive balanced search strategy is proposed to improve the upward growth mathematical model of the Ivy algorithm. The influence weight of the optimal solution on the update position is adjusted by the nonlinear change of the adaptive factor β. The position update of the search agent is balanced by the probability P. The upward growth mathematical model improved by the adaptive balanced search strategy is shown in formula (4): I new =I best ·β+p·I best ·rand(1,dim)+(1-p)·I best ·Gv (4); In formula (4), I new Indicates the updated search agent location, I best represents the optimal position at the current stage, β is the adaptive factor as shown in formula (5), p represents the update probability, which is a random number between [0, 1], dim is the problem dimension, and Gv represents the ivy growth rate as shown in formula (2); β＝α·e -iter / max iter (5)； In formula (5), α indicates that the initial impact factor is 0.5, iter indicates the current iteration number, and maxiter indicates the maximum iteration number; S3. Use the improved Ivy algorithm to adjust the parameters of the PID controller of the speed loop and the current loop, and obtain the best three parameters Kp, Ki, and Kd through algorithm optimization; S4. Input the three optimal PID parameters adjusted by the ivy algorithm into the permanent magnet coreless motor simulation control system of the refrigeration unit compressor to optimize the control effect of the permanent magnet coreless motor.

2. The control optimization method of a permanent magnet coreless motor for a refrigeration unit compressor according to claim 1, characterized in that: In the step S1, the permanent magnet coreless motor simulation control system of the refrigeration unit compressor first inputs the desired speed as an input signal into the control system, transmits the actual speed detected by the speed detection module and the desired speed together to the error calculation module, transmits the obtained error e1(t) to the speed loop PID control module, performs parameter adjustment through the improved ivy algorithm, and transmits the control quantity u1(t) and the actual current detected by the current detection module together to the error calculation module, transmits the error e2(t) to the current loop PID control module, performs parameter adjustment through the improved ivy algorithm, and transmits the control quantity u2(t) to the permanent magnet coreless motor module for speed control.

3. The control optimization method of a permanent magnet coreless motor for a refrigeration unit compressor according to claim 2, characterized in that: In S3, the parameters of the PID controllers of the speed loop and the current loop are adjusted using the improved ivy algorithm, and the specific steps of obtaining the optimal three parameters Kp, Ki, and Kd by optimizing the algorithm are as follows: S31, encoding the parameters Kp, Ki, Kd of the PID controller into an Ivy algorithm search space solution; S32, initialize the parameters of the improved Ivy algorithm, including the algorithm's population size N, problem dimension dim, maximum number of iterations maxiter, search space upper limit ub, and contraction space lower limit lb; S33, initialize the population, calculate the fitness values ​​of the individuals in the Ivy Algorithm population, sort them according to the size of the fitness values, and determine the best individual in the initial population. The calculation formula of the fitness value is: In formula (6), exp(t) represents the expected output value at time t, act(t) represents the actual output value at time t, T represents the total time of system operation, and J represents the fitness value; S34, updating the population position by improving the mathematical model of the Ivy algorithm, and searching for individuals with the best fitness value through the population update; S35. Determine whether the current number of iterations has reached the maximum number of iterations. If not, continue to search for the best solution. If so, exit the optimization iteration and use the output optimal solution as the optimal Kp, Ki, and Kd parameter values ​​of the PID controller. Input the parameter values ​​into the permanent magnet coreless motor simulation control system of the refrigeration unit compressor to complete the control of the system.

4. The control optimization method of a permanent magnet coreless motor for a refrigeration unit compressor according to claim 3 is characterized in that: In S34, the specific steps of updating the population position by improving the mathematical model of the Ivy algorithm are: Step 1: Calculate the growth rate Gv of individuals in the population. The calculation formula is shown in formula (2). Calculate the random parameter φ. rand is a random number between [0,1]; Step 2: If the fitness value of the current position is less than φ multiplied by the fitness value of the best position of the current iteration, the improved extended growth mathematical model is executed to update the population position. The specific formula is shown in formula (1); Step 3: If the fitness value of the current position is greater than or equal to φ multiplied by the fitness value of the best position of the current iteration, the improved upward growth mathematical model is executed to update the population position. The specific formula is shown in formula (4); Step 4: Evaluate the fitness value of the new position, update the best position of the population, and proceed to the next iteration.

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