Automatic bearing press-in force control method

Abstract

The invention discloses an automatic bearing press-in force control method, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: S1, constructing an automatic bearing press-in force control system; s2, improving a human memory optimization algorithm, wherein the specific improvement strategy is as follows: S21, improving a mathematical model of a memory generation stage of the human memory optimization algorithm; s22, improving an emotional state factor beta in a memory behavior stage in a human memory optimization algorithm through a self-adaptive Gaussian disturbance strategy; s3, converting the automatic bearing press-in force control system into a mathematical model to be optimized, and optimizing a PID (Proportion Integration Differentiation) controller through an improved human memory optimization algorithm; and S4, the optimized parameter values are used for the automatic bearing press-in force control system, the bearing press-in force is adjusted and stabilized near the target pressure value, and the stability and the control performance of the system are enhanced by optimizing the automatic bearing press-in force control system through an improved algorithm.

Description

Technical Field

[0001] The invention belongs to the technical field of PID control optimization, and in particular relates to an automatic bearing pressing force control method. Background Art

[0002] Automated bearing press force control technology is an advanced control system that integrates sensors, actuators and control algorithms. It monitors the pressure applied to the bearing in real time and compares it with the set value, automatically adjusting the output of the actuator to ensure that the pressure applied during the assembly process always remains within the optimal range, thereby preventing bearing damage or improper installation caused by excessive or insufficient pressure, thereby improving product quality, reducing failure rates and increasing production efficiency. It is widely used in the bearing assembly process in the automotive, aviation and machinery manufacturing industries, and PID controllers are usually used to control the pressure.

[0003] PID controller is a feedback controller widely used in industrial control systems. It aims to adjust the control input so that the system output reaches the set target. It consists of three parts: proportional (P), integral (I) and differential (D). The proportional part is adjusted according to the current error (the difference between the set value and the actual value), the integral part accumulates past errors to eliminate steady-state errors, and the differential part predicts future error changes to improve the response speed of the system. By reasonably adjusting these three parameters, the PID controller can achieve fast and stable system response and is widely used in various control occasions such as temperature, pressure, and flow.

[0004] Human Memory Optimization (HMO) is an algorithm that simulates human behavior in production, stores preferences for success and failure, and simulates human memory, gradually moving toward better directions and results to find a reasonable optimal solution. By comparing with other metaheuristic algorithms on the CEC 2013 test set, the results show that HMO has better optimization capabilities, and the feasibility of the algorithm is verified through convergence analysis and parameter analysis experiments. Using the human memory optimization algorithm to optimize the PID controller parameters can not only improve the performance of the control system, but also enhance the adaptability and search efficiency of the algorithm, providing an effective solution for complex control problems. Summary of the invention

[0005] The purpose of the invention is to use an improved human memory optimization algorithm to optimize the PID controller in an automated bearing pressure control system, thereby improving the control accuracy and adaptability of the control system, quickly finding the optimal parameter settings, reducing dependence on manual adjustments, enhancing system stability, reducing equipment damage due to excessive pressure, and improving overall performance and product production quality, thereby ensuring the correct installation of bearings and improving production efficiency.

[0006] In order to achieve the above object, the present invention adopts the following technical solution.

[0007] S1. Construct an automated bearing press force control system. The control system is divided into a press force controller, a force sensor, a motor drive system, and a data acquisition and preprocessing module. The press force controller is composed of an improved human memory optimization algorithm and a PID controller. The PID control is optimized by the improved human memory optimization algorithm to achieve precise control of the pressure. The force sensor collects the press force applied during the bearing installation process in real time. The motor drive system serves as a driving force device and adjusts the force according to the output of the controller. The data acquisition and processing module processes the force sensor data and provides feedback for the press force controller.

[0008] S2. Improve the human memory optimization algorithm. The specific improvement strategies are: S21. Improve the mathematical model of the memory generation phase of the human memory optimization algorithm. By introducing a differential mutation strategy, the population position updated in the memory generation phase of the original algorithm is differentially mutated, and the intensity of the mutation is dynamically adjusted through an adaptive mutation probability factor. The specific mutation strategy steps are as follows: First, generate a random judgment factor , if the judgment factor is greater than the set mutation probability factor Then mutation is executed. If mutation is executed, the fitness value of the individual after mutation needs to be calculated. If the fitness value of the individual after mutation is less than the fitness value of the individual before mutation, the individual after mutation is accepted, otherwise the original individual position before mutation is retained; S22. An adaptive Gaussian perturbation strategy is used to improve the emotional state factor β in the recall behavior stage of the human memory optimization algorithm. The factor is dynamically adjusted according to the fitness value in the optimization process and the current number of iterations. The emotional state factor is randomly perturbed by adding Gaussian distribution.

[0009] S3. Convert the automated bearing pressure control system into a mathematical model to be optimized, and then optimize the PID controller in the automated bearing pressure control system through an improved human memory optimization algorithm, and obtain an optimal set of Kp, Ki, and Kd parameter values ​​through optimization.

[0010] S4. The optimized parameter values ​​are used in the automated bearing pressure control system. The bearing pressure is adjusted by the optimized PID controller output control quantity and stabilized near the target pressure value.

[0011] Preferably, in said S1, the execution process of the automated bearing press force control system constructed is: first, the set target pressure and the actual pressure obtained by the force sensor are input into the data acquisition preprocessing module, the pressure value is converted into the corresponding pressure electrical signal, and the error e(t) between the target pressure electrical signal and the actual pressure electrical signal is input into the press force controller, the PID controller parameters are optimized by improving the human memory optimization algorithm and the optimal control quantity u(t) is output, the motor drive system adjusts the torque of the motor according to the control quantity and converts the torque into pressure through the rotation system, wherein the mathematical model of the data acquisition preprocessing module is shown in formula (1): (1); In formula (1), represents the time constant of the system, represents the pressure electrical signal, Ks represents the pressure sensor gain, and F(t) represents the pressure value; The motor drive system adjusts the current and thus the motor torque Tm by improving the control quantity u(t) output by the PID controller optimized by the human memory optimization algorithm, and converts the motor torque into the pressure value F(t) through the rotation system. The mathematical model of the control quantity u(t) is shown in formula (2), and the mathematical model of the pressure value F(t) is shown in formula (3): (2); In formula (2), u(t) represents the control amount, Kp represents the proportional gain, Ki represents the integral gain, Kd represents the differential gain, e(t) represents the real-time error, , represents the target pressure, Indicates the actual pressure value; (3); In formula (3), F(t) represents the pressure value, Tm represents the motor torque, r represents the equivalent radius of the rotating device, and μ represents the transmission ratio.

[0012] Preferably, in S21, the mathematical model of the memory generation phase of the human memory optimization algorithm is improved, and the population position updated in the memory generation phase of the original algorithm is differentially mutated by introducing a differential mutation strategy, and the intensity of the mutation is dynamically adjusted by an adaptive mutation probability factor. The improved mathematical model is: (4); In formula (4), X(iter+1) represents the updated individual position, represents the average position in the population, V represents the simulated human behavior state function, and the calculation formula is V=K•sin(1 / K)•b, K represents a random number between [0.1, 1.3], b represents the iteration factor, b=2•(1-iter / max_iter), iter represents the current number of iterations, max_iter represents the maximum number of iterations, UB represents the upper limit of the search space, LB represents the lower limit of the search space, rand(1,dim) represents a random number between [1, dim], and dim represents the dimension of the optimization problem. represents the mutation probability factor, and the calculation formula is shown in formula (5). represents the random judgment factor, X(iter) represents the current individual position, Indicates the maximum value of differential variation intensity, and They represent the positions of two individuals randomly selected from the population, represents the average position weighting factor, σ represents the random perturbation factor, and randn(1, daim) represents a random number matrix of standard normal distribution with dimension dim; (5); In formula (5), represents the mutation probability factor, represents the minimum value of the mutation probability, represents the maximum value of the mutation probability, represents the worst fitness value in the population, represents the optimal fitness value in the population, and f represents the fitness value of the current individual.

[0013] The advantage of this improved strategy is that by introducing differential mutation and dynamic adjustment mechanism, the mutation probability can be adaptively adjusted through the current individual fitness value, which can effectively balance the relationship between exploration and development in the algorithm optimization process, thereby improving the efficiency and convergence speed of the optimization process. At the same time, by utilizing the average position and random perturbations of the current solution, more flexible exploration can be made in the search space to avoid falling into the local optimal solution. Differential mutation also enhances the diversity and adaptability of the solution, making the algorithm perform better in complex problems.

[0014] Preferably, in S22, an adaptive Gaussian perturbation strategy is used to improve the emotional state factor β in the recall behavior stage of the human memory optimization algorithm, and the mathematical model of the improved emotional state factor β is: (6); In formula (6), β represents the emotional state factor, Represents the basic value of the emotional state factor, which is used to control the basic strength of β. represents the mean fitness in the population, It represents the optimal fitness value in the population, iter represents the current number of iterations, max_iter represents the maximum number of iterations, and k represents the random adjustment parameter. represents the Gaussian distribution function, represents the mean of the Gaussian distribution, Represents the standard deviation of the Gaussian distribution.

[0015] Preferably, the β emotion state factor in the original algorithm is a random number that follows the normal distribution. Although it has a certain diversity, it lacks adaptive ability when dealing with complex problems. After the strategy improvement in S22, the emotion state factor can be adaptively adjusted according to the current iteration stage and the distribution of the population fitness value. At the same time, combined with the disturbance of the Gaussian distribution, the randomness of the β update is enhanced. This improvement not only improves the update quality of β in the optimization process, but also ensures its diversity, thereby improving the overall optimization capability of the algorithm.

[0016] Preferably, in S3, the PID controller is optimized by an improved human memory optimization algorithm, and the specific steps of obtaining an optimal set of Kp, Ki, and Kd parameter values ​​by optimization are: S31. A second-order transfer function is constructed as the controlled model of the system through the dynamic characteristics of the automatic bearing pressure control system. The specific formula is: (7); In formula (7), G(s) represents the transfer function, Ks represents the gain of the system, and s represents the complex frequency domain variable in the Laplace transform. and represents the time constant of the system; S32, initializing the parameters of the improved human memory optimization algorithm, including the number of populations N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit UB and the lower limit LB of the search space, and generating the initial population position by improving the initial parameters of the human memory optimization algorithm; S33, mapping the solution vector [x1, x2, x3] in the improved human memory optimization algorithm to the parameters of the PID controller of the automated bearing pressure force control system, the problem dimension to be optimized dim=3, that is, x1 corresponds to the proportional gain Kp, x2 corresponds to the integral gain Ki, and x3 corresponds to the differential gain Kd. The population position changes continuously in the algorithm optimization, thereby adjusting the parameters of the PID control, and achieving the optimal response by continuously adjusting and optimizing the PID; S34, constructing a fitness value function in an improved human memory optimization algorithm, calculating the fitness values ​​of individuals in the initial population through the fitness value function, sorting, selecting the best position and the worst position in the population, and adding the best position and the worst position to the best memory matrix and the worst memory matrix respectively. The specific fitness value calculation function is: (8); In formula (8), J represents the fitness value, T represents the system operation time, represents the target pressure, Indicates the actual pressure value; S35, by improving the mathematical model of the human memory optimization algorithm, the positions of the individuals in the population are updated, and the fitness values ​​of the individuals in the population, the ranking of the individual fitness values, the worst memory matrix and the best memory matrix are updated. The specific steps are: Step 1: First, execute the memory generation phase of the algorithm. The mathematical model of this phase is shown in formula (4). Set the probability value r = 0.3 and generate a random number rand between [0, 1]. Step 2: If rand <= r, execute the stage of human recall failure event. The mathematical model of this stage is shown in formula (9): (9); In formula (9), X(iter+1) represents the updated individual position, represents the average position in the population, Xbest represents the optimal position in the population, β represents the emotional state factor, r1 and r2 represent random numbers between [0, 1], represents the αth position selected in the worst memory matrix; Step 3: If rand>r and R<=0.5, execute the stage of human recall of recent events. The mathematical model of this stage is shown in formula (10): (10); In formula (10), X(iter+1) represents the updated individual position, represents the average position in the population, σ and r1 represent random numbers between [0,1], represents a random individual position in the population, and Pworst represents the worst position under the current number of iterations; Step 4: If rand>0.5 and rand<=1-r, execute the stage of human searching for lost memory. The mathematical model of this stage is shown in formula (11): (11); In formula (11), X(iter+1) represents the updated individual position, Xbest represents the optimal position in the population, r2 represents a random number between [0,1], and X(iter) represents the current individual position. Indicates the optimal position under the current number of iterations; Step 5: Finally, the stage of human recall of successful events is executed. The mathematical model of this stage is shown in formula (12): (12); In formula (12), X(iter+1) represents the updated individual position, Xbest represents the optimal position in the population, r1 and r2 represent random numbers between [0, 1], and β represents the emotional state factor. The calculation formula is shown in formula (6). represents the randomly selected α2th position in the optimal memory matrix, represents the average position in the population; S36, determine whether the current number of iterations has reached the maximum number of iterations, if it has reached the maximum number of iterations, complete the optimization and output the optimal solution, otherwise, continue to execute S35 to perform optimization.

[0017] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: by improving the human memory optimization algorithm, the adaptability of the algorithm in the optimization process is enhanced, the optimization capabilities of the algorithm in the exploration and development stages are balanced, the convergence speed and optimization accuracy of the algorithm are improved, and when the algorithm falls into a local optimal solution, it can quickly jump out and continue to optimize, showing better robustness in dealing with complex problems. The improved human memory optimization algorithm is used to optimize the PID controller in the automated bearing pressure force control system, thereby improving the control accuracy and adaptability of the automated bearing pressure force control system. This method can quickly find the optimal control parameters and reduce dependence on manual adjustment, thereby enhancing the stability of the system and reducing the risk of equipment damage due to excessive pressure. The improvement in overall control performance helps to improve the production quality of products, ensure the correct installation of bearings, and further improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of an automated bearing press-in force control method.

[0019] Figure 2 This is a model diagram of the automated bearing press force control system.

[0020] Figure 3 Optimizing algorithm execution flow chart for human memory.

[0021] Figure 4 A comparison chart of the changes in fitness values ​​during the optimization process of the improved human memory optimization algorithm and the standard human memory optimization algorithm.

[0022] Figure 5 Comparison chart of the effects of optimizing PID controller using improved human memory optimization algorithm and standard human memory optimization algorithm. DETAILED DESCRIPTION

[0023] 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 belong to the scope of protection of the present invention.

[0024] The present invention provides a technical solution: an automated bearing press force control method, specifically comprising the following steps: Figure 1 shown.

[0025] S1. Build an automated bearing press force control system, the control system is divided into a press force controller, a force sensor, a motor drive system, and a data acquisition preprocessing module, such as Figure 2 As shown, the pressing force controller is composed of an improved human memory optimization algorithm and a PID controller. The PID control is optimized by the improved human memory optimization algorithm to achieve precise control of the pressure. The force sensor collects the pressing force applied during the bearing installation process in real time. The motor drive system serves as a driving force device and adjusts the force according to the output of the controller. The data acquisition and processing module processes the force sensor data to provide feedback for the pressing force controller.

[0026] Furthermore, in said S1, the execution process of the automated bearing press force control system constructed is as follows: first, the set target pressure and the actual pressure obtained by the force sensor are input into the data acquisition preprocessing module, the pressure value is converted into the corresponding pressure electrical signal, and the error e(t) between the target pressure electrical signal and the actual pressure electrical signal is input into the press force controller, the PID controller parameters are optimized by improving the human memory optimization algorithm and the optimal control quantity u(t) is output, the motor drive system adjusts the torque of the motor according to the control quantity and converts the torque into pressure through the rotation system, wherein the mathematical model of the data acquisition preprocessing module is shown in formula (1): (1); In formula (1), represents the time constant of the system, represents the pressure electrical signal, Ks represents the pressure sensor gain, and F(t) represents the pressure value; The motor drive system adjusts the current and thus the motor torque Tm by improving the control quantity u(t) output by the PID controller optimized by the human memory optimization algorithm, and converts the motor torque into the pressure value F(t) through the rotation system. The mathematical model of the control quantity u(t) is shown in formula (2), and the mathematical model of the pressure value F(t) is shown in formula (3): (2); In formula (2), u(t) represents the control amount, Kp represents the proportional gain, Ki represents the integral gain, Kd represents the differential gain, e(t) represents the real-time error, , represents the target pressure, Indicates the actual pressure value; (3); In formula (3), F(t) represents the pressure value, Tm represents the motor torque, r represents the equivalent radius of the rotating device, and μ represents the transmission ratio.

[0027] S2. Improve the human memory optimization algorithm. The specific improvement strategies are: S21. Improve the mathematical model of the memory generation phase of the human memory optimization algorithm. By introducing a differential mutation strategy, the population position updated in the memory generation phase of the original algorithm is differentially mutated, and the intensity of the mutation is dynamically adjusted through an adaptive mutation probability factor. The specific mutation strategy steps are as follows: First, generate a random judgment factor , if the judgment factor is greater than the set mutation probability factor Then mutation is executed. If mutation is executed, the fitness value of the individual after mutation needs to be calculated. If the fitness value of the individual after mutation is less than the fitness value of the individual before mutation, the individual after mutation is accepted, otherwise the original individual position before mutation is retained; S22. An adaptive Gaussian perturbation strategy is used to improve the emotional state factor β in the recall behavior stage of the human memory optimization algorithm. The factor is dynamically adjusted according to the fitness value in the optimization process and the current number of iterations. The emotional state factor is randomly perturbed by adding Gaussian distribution.

[0028] Furthermore, in S21, the mathematical model of the memory generation phase of the human memory optimization algorithm is improved, and the population position updated in the memory generation phase of the original algorithm is differentially mutated by introducing a differential mutation strategy, and the intensity of the mutation is dynamically adjusted by an adaptive mutation probability factor. The improved mathematical model is: (4); In formula (4), X(iter+1) represents the updated individual position, represents the average position in the population, V represents the simulated human behavior state function, and the calculation formula is V=K•sin(1 / K)•b, K represents a random number between [0.1, 1.3], b represents the iteration factor, b=2•(1-iter / max_iter), iter represents the current number of iterations, max_iter represents the maximum number of iterations, UB represents the upper limit of the search space, LB represents the lower limit of the search space, rand(1,dim) represents a random number between [1, dim], and dim represents the dimension of the optimization problem. represents the mutation probability factor, and the calculation formula is shown in formula (5). represents the random judgment factor, X(iter) represents the current individual position, Indicates the maximum value of differential variation intensity, and They represent the positions of two individuals randomly selected from the population, represents the average position weighting factor, σ represents the random perturbation factor, and randn(1, daim) represents a random number matrix of standard normal distribution with dimension dim; (5); In formula (5), represents the mutation probability factor, represents the minimum value of the mutation probability, represents the maximum value of the mutation probability, represents the worst fitness value in the population, represents the optimal fitness value in the population, and f represents the fitness value of the current individual.

[0029] Furthermore, in S22, an adaptive Gaussian perturbation strategy is used to improve the emotional state factor β in the recall behavior stage of the human memory optimization algorithm, and the mathematical model of the improved emotional state factor β is: (6); In formula (6), β represents the emotional state factor, Represents the basic value of the emotional state factor, which is used to control the basic strength of β. represents the mean fitness in the population, It represents the optimal fitness value in the population, iter represents the current number of iterations, max_iter represents the maximum number of iterations, and k represents the random adjustment parameter. represents the Gaussian distribution function, represents the mean of the Gaussian distribution, Represents the standard deviation of the Gaussian distribution.

[0030] S3. Convert the automated bearing pressure control system into a mathematical model to be optimized, and then optimize the PID controller in the automated bearing pressure control system through an improved human memory optimization algorithm, and obtain an optimal set of Kp, Ki, and Kd parameter values ​​through optimization.

[0031] Furthermore, in S3, the PID controller is optimized by using an improved human memory optimization algorithm, and the specific steps of obtaining an optimal set of Kp, Ki, and Kd parameter values ​​by searching for the optimal value are: S31. A second-order transfer function is constructed as the controlled model of the system through the dynamic characteristics of the automatic bearing pressure control system. The specific formula is: (7); In formula (7), G(s) represents the transfer function, Ks represents the gain of the system, and s represents the complex frequency domain variable in the Laplace transform. and represents the time constant of the system; S32, initializing the parameters of the improved human memory optimization algorithm, including the number of populations N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit UB and the lower limit LB of the search space, and generating the initial population position by using the initial parameters of the improved human memory optimization algorithm; S33, mapping the solution vector [x1, x2, x3] in the improved human memory optimization algorithm to the parameters of the PID controller of the automated bearing pressure force control system, the problem dimension to be optimized dim=3, that is, x1 corresponds to the proportional gain Kp, x2 corresponds to the integral gain Ki, and x3 corresponds to the differential gain Kd. The population position changes continuously in the algorithm optimization, thereby adjusting the parameters of the PID control, and achieving the optimal response by continuously adjusting and optimizing the PID; S34, constructing a fitness value function in an improved human memory optimization algorithm, calculating the fitness values ​​of individuals in the initial population through the fitness value function, sorting, selecting the best position and the worst position in the population, and adding the best position and the worst position to the best memory matrix and the worst memory matrix respectively. The specific fitness value calculation function is: (8); In formula (8), J represents the fitness value, T represents the system operation time, represents the target pressure, Indicates the actual pressure value; S35, by improving the mathematical model of the human memory optimization algorithm, the positions of individuals in the population are updated, and the fitness values ​​of individuals in the population, as well as the ranking of individual fitness values, the worst memory matrix and the best memory matrix are updated. The specific steps are as follows: Figure 3 As shown: Step 1: First, execute the memory generation phase of the algorithm. The mathematical model of this phase is shown in formula (4). Set the probability value r = 0.3 and generate a random number rand between [0, 1]. Step 2: If rand <= r, execute the stage of human recall failure event. The mathematical model of this stage is shown in formula (9): (9); In formula (9), X(iter+1) represents the updated individual position, represents the average position in the population, Xbest represents the optimal position in the population, β represents the emotional state factor, r1 and r2 represent random numbers between [0, 1], represents the αth position selected in the worst memory matrix; Step 3: If rand>r and R<=0.5, execute the stage of human recall of recent events. The mathematical model of this stage is shown in formula (10): (10); In formula (10), X(iter+1) represents the updated individual position, represents the average position in the population, σ and r1 represent random numbers between [0,1], represents a random individual position in the population, and Pworst represents the worst position under the current number of iterations; Step 4: If rand>0.5 and rand<=1-r, execute the stage of human searching for lost memory. The mathematical model of this stage is shown in formula (11): (11); In formula (11), X(iter+1) represents the updated individual position, Xbest represents the optimal position in the population, r2 represents a random number between [0,1], and X(iter) represents the current individual position. Indicates the optimal position under the current number of iterations; Step 5: If rand>1-r, execute the stage of human recall of successful events. The mathematical model of this stage is shown in formula (12): (12); In formula (12), X(iter+1) represents the updated individual position, Xbest represents the optimal position in the population, r1 and r2 represent random numbers between [0, 1], and β represents the emotional state factor. The calculation formula is shown in formula (6). represents the randomly selected α2th position in the optimal memory matrix, represents the average position in the population; S36, determine whether the current number of iterations has reached the maximum number of iterations, if it has reached the maximum number of iterations, complete the optimization and output the optimal solution, otherwise, continue to execute S35 to perform optimization.

[0032] S4. The optimized parameter values ​​are used in the automated bearing pressure control system. The bearing pressure is adjusted by the optimized PID controller output control quantity and stabilized near the target pressure value.

[0033] Furthermore, the mathematical model of the human memory optimization algorithm is improved by Matlab, and the code design and writing of the experiment of the method of the present invention are completed. The parameters of the improved human memory optimization algorithm are initialized, the maximum number of iterations max_iter=30, the population size is 200, and the search space range is [80, 0.001]. The automatic bearing pressure control system is constructed by the simulation software Simulink. The constructed model includes: a pressure controller, a force sensor, a motor drive system, and a data acquisition preprocessing module. The pressure controller is composed of an improved human memory optimization algorithm and a PID controller. The controlled model of the automatic bearing pressure control system is Laplace transformed as shown in formula (7). The specific parameter values ​​are given according to the actual operating state of the system. The gain Ks of the system is taken as 20, and the time constant of the system is and The values ​​are 5 and 10 respectively, and the specific controlled model formula is: , set up the experimental code in Matlab to combine the algorithm with the constructed automatic bearing pressure control system model, run the code and model program, and the experimental results are as follows Figure 4-5 shown.

[0034] Furthermore, Figure 4 This is a comparison chart of the fitness value changes during the optimization process of the improved human memory optimization algorithm and the standard human memory optimization algorithm. From the figure, it can be seen that the improved human memory optimization algorithm has better adaptability during the optimization process. When it falls into a local optimal solution, it can quickly jump out and continue to optimize, and the fitness value obtained by the optimization is smaller, indicating that the improved algorithm has higher optimization accuracy and better robustness. Figure 5 The comparison chart of the effects of optimizing PID controller by improved human memory optimization algorithm and standard human memory optimization algorithm is shown. The target value of the PID controller response value is set to 1 unit. It can be seen from the figure that the PID controller optimized by the improved algorithm has smaller overshoot and better stability. Compared with the PID controller optimized by the original algorithm, the PID control optimized by the improved algorithm can reach the target value and be stable in a shorter time, indicating that the overall performance of the automated bearing pressure force control system optimized by the improved human memory optimization algorithm is better.

Claims

1. An automated bearing press force control method, characterized in that: The specific steps are as follows: S1. Construct an automated bearing press force control system, the control system is divided into a press force controller, a force sensor, a motor drive system, and a data acquisition preprocessing module, wherein the press force controller is composed of an improved human memory optimization algorithm and a PID controller, and the PID control is optimized by the improved human memory optimization algorithm to achieve precise control of the pressure, the force sensor collects the press force applied during the bearing installation process in real time, the motor drive system serves as a driving force device, and adjusts the force according to the output of the controller, and the data acquisition and processing module processes the force sensor data to provide feedback for the press force controller; S2. Improve the human memory optimization algorithm. The specific improvement strategies are: S21. Improve the mathematical model of the memory generation phase of the human memory optimization algorithm. By introducing a differential mutation strategy, the population position updated in the memory generation phase of the original algorithm is differentially mutated, and the intensity of the mutation is dynamically adjusted through an adaptive mutation probability factor. The specific mutation strategy steps are as follows: First, generate a random judgment factor , if the judgment factor is greater than the set mutation probability factor Then mutation is executed. If mutation is executed, the fitness value of the individual after mutation needs to be calculated. If the fitness value of the individual after mutation is less than the fitness value of the individual before mutation, the individual after mutation is accepted, otherwise the original individual position before mutation is retained; S22. Improve the emotional state factor β in the recall behavior stage of the human memory optimization algorithm through an adaptive Gaussian perturbation strategy, dynamically adjust it according to the fitness value and the current number of iterations in the optimization process, and randomly perturb the emotional state factor by adding Gaussian distribution; S3, converting the automated bearing pressure-in force control system into a mathematical model to be optimized, and then optimizing the PID controller in the automated bearing pressure-in force control system through an improved human memory optimization algorithm, and obtaining an optimal set of Kp, Ki, and Kd parameter values ​​through optimization; S4. The optimized parameter values ​​are used in the automated bearing pressure control system. The bearing pressure is adjusted by the optimized PID controller output control quantity and stabilized near the target pressure value.

2. The method for controlling the automatic bearing pressing force according to claim 1, characterized in that: In the S1, the execution process of the constructed automated bearing pressure control system is: first, the set target pressure and the actual pressure obtained by the force sensor are input into the data acquisition preprocessing module, the pressure value is converted into the corresponding pressure electrical signal, and the error e(t) between the target pressure electrical signal and the actual pressure electrical signal is input into the pressure controller, the PID controller parameters are optimized by improving the human memory optimization algorithm and the optimal control quantity u(t) is output, and the motor drive system adjusts the torque of the motor according to the control quantity and converts the torque into pressure through the rotation system.

3. The method for controlling the automatic bearing pressing force according to claim 2, characterized in that: In S21, the mathematical model of the memory generation phase of the human memory optimization algorithm is improved, and the population position updated in the memory generation phase of the original algorithm is differentially mutated by introducing a differential mutation strategy, and the intensity of the mutation is dynamically adjusted by an adaptive mutation probability factor. The improved mathematical model is: (4)； In formula (4), X(iter+1) represents the updated individual position, represents the average position in the population, V represents the simulated human behavior state function, and the calculation formula is V=K•sin(1 / K)•b, K represents a random number between [0.1, 1.3], b represents the iteration factor, b=2•(1-iter / max_iter), iter represents the current number of iterations, max_iter represents the maximum number of iterations, UB represents the upper limit of the search space, LB represents the lower limit of the search space, rand(1,dim) represents a random number between [1, dim], and dim represents the dimension of the optimization problem. represents the mutation probability factor, and the calculation formula is shown in formula (5). represents the random judgment factor, X(iter) represents the current individual position, Indicates the maximum value of differential variation intensity, and They represent the positions of two individuals randomly selected from the population, represents the average position weighting factor, σ represents the random perturbation factor, and randn(1, daim) represents a random number matrix of standard normal distribution with dimension dim; (5)； In formula (5), represents the mutation probability factor, represents the minimum value of the mutation probability, represents the maximum value of the mutation probability, represents the worst fitness value in the population, represents the optimal fitness value in the population, and f represents the fitness value of the current individual.

4. The method for controlling the automatic bearing pressing force according to claim 3, characterized in that: In S22, an adaptive Gaussian perturbation strategy is used to improve the emotional state factor β in the recall behavior stage of the human memory optimization algorithm. The mathematical model of the improved emotional state factor β is: (6)； In formula (6), β represents the emotional state factor, Represents the basic value of the emotional state factor, which is used to control the basic strength of β. represents the mean fitness in the population, It represents the optimal fitness value in the population, iter represents the current number of iterations, max_iter represents the maximum number of iterations, and k represents the random adjustment parameter. represents the Gaussian distribution function, represents the mean of the Gaussian distribution, Represents the standard deviation of the Gaussian distribution.

5. The method for controlling the automatic bearing pressing force according to claim 4, characterized in that: In S3, the PID controller is optimized by using an improved human memory optimization algorithm, and the specific steps of obtaining an optimal set of Kp, Ki, and Kd parameter values ​​by searching for the best value are as follows: S31. A second-order transfer function is constructed as the controlled model of the system through the dynamic characteristics of the automatic bearing pressure control system. The specific formula is: (7)； In formula (7), G(s) represents the transfer function, Ks represents the gain of the system, and s represents the complex frequency domain variable in the Laplace transform. and represents the time constant of the system; S32, initializing the parameters of the improved human memory optimization algorithm, including the number of populations N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit UB and the lower limit LB of the search space, and generating the initial population position by improving the initial parameters of the human memory optimization algorithm; S33, mapping the solution vector [x1, x2, x3] in the improved human memory optimization algorithm to the parameters of the PID controller of the automated bearing pressure force control system, the problem dimension to be optimized dim=3, that is, x1 corresponds to the proportional gain Kp, x2 corresponds to the integral gain Ki, and x3 corresponds to the differential gain Kd. The population position changes continuously in the algorithm optimization, thereby adjusting the parameters of the PID control, and achieving the optimal response by continuously adjusting and optimizing the PID; S34, constructing a fitness value function in an improved human memory optimization algorithm, calculating the fitness values ​​of individuals in the initial population through the fitness value function, sorting, selecting the best position and the worst position in the population, and adding the best position and the worst position to the best memory matrix and the worst memory matrix respectively. The specific fitness value calculation function is: (8)； In formula (8), J represents the fitness value, T represents the system operation time, Indicates the target pressure, Indicates the actual pressure value; S35, updating the positions of individuals in the population by improving the mathematical model of the human memory optimization algorithm, and updating the fitness values ​​of individuals in the population as well as the ranking of individual fitness values, the worst memory matrix and the best memory matrix; S36, determine whether the current number of iterations has reached the maximum number of iterations, if it has reached the maximum number of iterations, complete the optimization and output the optimal solution, otherwise, continue to execute S35 to perform optimization.

Images