Control method based on vehicle-mounted equipment rotation system

Abstract

The invention discloses a control method based on a vehicle-mounted equipment rotation system, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: 1, constructing a PID control model based on the vehicle-mounted equipment rotation system, which comprises an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a PID controller, a space vector pulse width modulation model and an observer model; step 2, improving a dung beetle optimization algorithm, wherein the specific implementation is as follows: D1, processing a dung beetle individual position cross-border problem by using a boundary reflex method; d2, introducing successful experience of learning other dung beetles in the dung beetle stealing process, and guiding the stealing behavior through the historical optimal position; step 3, optimizing a current inner loop PID controller of the permanent magnet synchronous motor control system by using an improved dung beetle optimization algorithm to obtain optimal Kp, Ki and Kd control parameters of the current inner loop PID controller; and step 4, modeling simulation is carried out on the control of the vehicle-mounted equipment rotation system by adopting MATLAB (Matrix Laboratory).

Description

Technical Field

[0001] The invention belongs to the technical field of PID control, and in particular relates to a control method based on a vehicle-mounted equipment rotation system. Background Art

[0002] Automobile intelligence is an important development direction at present. The automatic driving function has gradually developed from the initial assisted driving to a higher level of automatic driving and even unmanned driving. In this process, the vehicle-mounted equipment rotation system needs to have higher precision and response speed to adapt to the instructions of the automatic driving system. The vehicle-mounted rotation system is widely used in military radar, engineering machinery, fire PTZ and other fields, and needs to achieve high-precision angle and speed control under complex working conditions.

[0003] The PID control technology of permanent magnet synchronous motor is a key motor control strategy. PID stands for proportional, integral and differential. During the control process, the proportional link outputs the control signal in proportion to the error between the actual operating state and the expected state of the motor, and can quickly respond to the error to adjust the motor state; the integral link accumulates the error to eliminate the steady-state error of the system and allow the motor output to reach the set value more accurately; the differential link adjusts the control amount according to the rate of change of the error. It can predict the development trend of the error, change the control signal in advance, effectively improve the dynamic response of the system, and reduce overshoot, thereby enabling the permanent magnet synchronous motor to achieve precise and stable control in terms of speed, torque or position.

[0004] Traditional PID control has the problems of fixed parameters and poor adaptability. The parameters rely on manual experience and lack robustness, making it difficult to meet the needs of dynamic load changes. When the dynamic characteristics of the vehicle-mounted equipment rotation system change, such as load changes, increased interference, etc., the fixed PID parameters cannot guarantee good control performance, and the input voltage or current of the motor cannot be adjusted in time and effectively to restore the speed, resulting in a decrease in the control accuracy of the system and even instability.

[0005] The dung beetle optimization algorithm is a new type of intelligent optimization algorithm, which is inspired by the rolling and foraging behaviors of dung beetles (commonly known as dung beetles). In nature, dung beetles roll animal feces into balls and push them to move in a specific direction. At the same time, they also look for high-quality food resources. By simulating the five stages of dung beetles' rolling, dancing, foraging, stealing, and reproduction, we can fully demonstrate the survival behavior characteristics of dung beetles in the natural environment, design an efficient optimization algorithm, and solve complex optimization problems. However, some parameters in the algorithm have a great impact on the performance of the algorithm, and show high sensitivity under different test functions, which means that in practical applications, parameters need to be adjusted carefully, which increases the difficulty of use and the computational cost. Summary of the invention

[0006] The purpose of the present invention is to achieve high-precision speed control of the vehicle-mounted equipment rotation system under complex working conditions. This paper proposes an improved dung beetle optimization algorithm to optimize the parameters of the motor current inner loop PID controller of the vehicle-mounted equipment rotation system, which solves the problems of fixed parameters, poor adaptability and poor control accuracy of the vehicle-mounted equipment rotation system of the traditional PID control method, thereby improving the robustness of the vehicle-mounted equipment rotation system.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A control method based on a vehicle-mounted equipment rotation system is characterized in that the control parameters of the vehicle-mounted equipment rotation system are optimized by improving the dung beetle optimization algorithm. The specific steps are as follows:

[0008] Step 1: Construct a PID control model based on the vehicle-mounted equipment rotation system, including an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a current inner loop PID controller, a space vector pulse width modulation model, and an observer model.

[0009] Step 2: Improve the dung beetle optimization algorithm, which is specifically implemented as follows: D1: Use the boundary reflection method to deal with the problem of dung beetle individual position crossing the boundary; D2: Introduce the successful experience of other dung beetles in the stealing process of dung beetles and guide the stealing behavior through the historical optimal position.

[0010] Step three, use the improved dung beetle optimization algorithm to optimize the current inner loop PID controller of the vehicle-mounted equipment rotation system, and obtain the optimal Kp, Ki, and Kd control parameters of the current inner loop PID controller.

[0011] Step 4: Use MATLAB to model and simulate the control of the vehicle-mounted equipment rotation system.

[0012] Furthermore, in the step one, a PID control model based on the vehicle-mounted equipment rotary system is constructed, including an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a PID controller, a space vector pulse width modulation model, and an observer model, characterized in that the improved dung beetle optimization algorithm model is improved on the basis of the traditional dung beetle optimization algorithm to better adapt to the complex environment and control requirements of the vehicle-mounted equipment rotary system; the permanent magnet synchronous motor control model is the power core of the vehicle-mounted equipment rotary system, and a motor control model is established according to the motion equation of the permanent magnet synchronous motor to observe the changes in the motor speed and position over time. Law, to achieve precise control of the vehicle-mounted equipment rotation system; PID controller is the core link of the entire rotation system control, according to the error between the input and output of the system, through the linear combination of the three links of proportion, integration and differentiation to generate the control signal; the space vector pulse width modulation model is used to convert the control signal output by the PID controller into the switching signal of the inverter, so as to control the stator voltage and current of the permanent magnet synchronous motor, thereby improving the control accuracy and performance of the permanent magnet synchronous motor; the observer model is based on the measurable input and output signals of the motor to estimate the state variables of the motor that are difficult to measure directly or with insufficient measurement accuracy.

[0013] Furthermore, the motion equation of the permanent magnet synchronous motor is an important equation that describes the mechanical motion characteristics of the motor. It establishes the relationship between the motor electromagnetic torque, load torque, the moment of inertia of the motor and load, the mechanical angular velocity and the viscous friction coefficient. The motion equation of the permanent magnet synchronous motor is: (1); In formula (1), is the electromagnetic torque, is the load torque, is the moment of inertia of the motor and load, is the mechanical angular velocity of the motor, is the viscous friction coefficient.

[0014] Furthermore, in the vehicle-mounted equipment slewing system, the PID controller acts in the current inner loop and becomes a current inner loop PID controller. The current inner loop PID controller receives the actual speed signal from the observer, compares it with the target speed, calculates the error, and then calculates the control signal according to the mathematical model of the current inner loop PID controller. The control signal is sent to the drive circuit of the permanent magnet synchronous motor to adjust the input current of the motor, thereby changing the speed of the motor, so that the actual state of the slewing system gradually approaches the desired state. The mathematical model of the current inner loop PID controller is: (2); In formula (2), is the control signal output by the current inner loop PID controller at time t, is the error between the expected input value and the actual output value at time t, , , are the proportional coefficient, integral coefficient and differential coefficient respectively.

[0015] Furthermore, the space vector pulse width modulation model can capture the changes in the output signal of the current inner loop PID controller. Through algorithm logic, it can accurately select the solution that best suits the current control needs from a large number of inverter switch state combinations and respond quickly.

[0016] Furthermore, the observer model focuses on the state variables of the motor that are difficult to measure directly or with poor measurement accuracy. It uses the measurable input and output signals of the motor to carry out estimation work, deeply associates the input voltage and current signals with the output speed and position feedback signals, and keenly captures the dynamic changes of the input and output signals during operation. Through the gain matrix, it continuously compares the difference between the actual output and the estimated output, and uses this to correct the estimated value of the state variable. The mathematical model of the observer is shown as follows: (3); In formula (3), is the estimated state, and , is the estimated output, and , is the gain matrix, and , is the actual output, To estimate the rate of change of state, is the input signal of the motor system, which is generally a measurable speed and position feedback signal. A, B, and C are the system matrix, input matrix, and output matrix, respectively.

[0017] Furthermore, in the step 2, a boundary reflection method is used to handle the problem of individual positions of dung beetles crossing the boundary, characterized in that when the position of an individual dung beetle exceeds the boundary of the search space, the boundary reflection method can be used to effectively pull it back into the legal search space. If the individual position is less than the lower boundary, it is reflected to a symmetrical position on the other side of the lower boundary. If the individual position is greater than the upper boundary, it is reflected to a symmetrical position on the other side of the upper boundary. The boundary reflection method is used to handle the problem of individual positions of dung beetles crossing the boundary, which solves the problems of invalid search, poor stability, and low population diversity caused by individual positions crossing the boundary in the original algorithm, and improves the algorithm performance. The mathematical model of the boundary reflection method is shown in the following formula: (4); In formula (4), is the original position of the i-th individual in the j-th dimension, is the position of the i-th individual in the j-th dimension after boundary reflection, is the lower limit of the search space in the jth dimension, is the upper limit of the search space in the jth dimension.

[0018] Furthermore, in the step 2, the successful experience of other dung beetles is introduced into the stealing process of the dung beetle, and the stealing behavior is guided by the historical optimal position. The characteristic is that it breaks the limitation of individual independent exploration and builds a bridge for group information interaction, so that each dung beetle no longer steals blindly and randomly, but captures potential high-quality solution areas based on the historical optimal positions accumulated by itself and its companions in past iterations, adjusts its own moving direction and distance, and moves closer to the optimal solution more accurately, which greatly improves the search efficiency and enables the algorithm to quickly focus on the global optimal solution. The mathematical model of this process is: (5); In formula (5), is the inertia weight, is the number of other dung beetles that participate in influencing the theft behavior of the i-th dung beetle, is the weight coefficient, is the global historical optimal position, is the position of the i-th dung beetle at the t-th iteration, is the historical optimal position of the j-th dung beetle at the t-th iteration.

[0019] Furthermore, in the step three, the improved dung beetle optimization algorithm is used to optimize the current inner loop PID controller of the vehicle-mounted equipment rotation system to obtain the optimal Kp, Ki, and Kd control parameters of the current inner loop PID controller. It is characterized in that the current inner loop PID controller mainly acts on the permanent magnet synchronous motor control, and then controls the vehicle-mounted equipment rotation system. The improved dung beetle optimization algorithm is used to optimize the parameter setting of the current inner loop PID controller, and the optimal Kp, Ki, and Kd parameters of the current inner loop PID controller are obtained by iteration, thereby optimizing the control performance of the current inner loop PID controller. The specific steps are: S1. Parameter initialization: Set the maximum number of iterations of the improved dung beetle optimization algorithm and population size , population dimension dim, search upper bound ub, lower bound lb, the initial position of the improved dung beetle optimization algorithm is the initial solution of the current inner loop PID parameters in the algorithm optimization process, and the position update of the dung beetle is the parameter update of the current inner loop PID controller; S2. Setting of the current inner loop PID parameters: By improving the dung beetle optimization algorithm, the Kp, Ki, and Kd parameter combination that achieves the best balance between the system dynamic response and steady-state performance is selected. Due to the time-sensitive characteristics of the improved dung beetle optimization algorithm, an indicator that can quantify the time cumulative error is required as the fitness function. The time multiplied by the absolute error integral can not only punish overshoot and oscillation due to its time-domain weighted characteristics of the error, but also effectively reflect the rapidity and steady-state accuracy of the system, thereby more accurately evaluating the comprehensive performance of the PID parameters. Its mathematical expression formula is as follows: (6); In formula (6), is a key indicator for measuring the deviation between the actual current and the expected reference current, and T is the cumulative effect used to integrate the error; S3, simulate the ball rolling and foraging behaviors of dung beetles: by simulating the five behavioral stages of ball rolling, dancing, foraging, stealing and reproduction of dung beetles, the position of dung beetle populations is dynamically adjusted to gradually approach the optimal solution; S4. Start iteration: Execute In each iteration optimization process, the current fitness value is compared with the historical optimal fitness value to determine whether to update the optimal fitness value. At the same time, the optimal solution of the group is updated according to the fitness value of each dung beetle individual. S5. Judgment: Determine whether the number of iterations t has reached the maximum number of iterations If it is reached, stop and output the optimal solution. If it is not reached, continue to execute steps S3 and S4; S6, assignment: assign the optimal solution to the three parameters Kp, Ki, and Kd of the current inner loop PID controller to complete the parameter optimization of the current inner loop PID controller.

[0020] Furthermore, in said S3, the ball rolling and foraging behaviors of dung beetles are simulated: by simulating the five behavioral stages of ball rolling, dancing, foraging, stealing and reproduction of dung beetles, the position of the dung beetle population is dynamically adjusted to gradually approach the optimal solution, which is characterized by integrating a unique mechanism of multiple behavioral stages, so that the algorithm exhibits efficient and flexible search capabilities in the search space: Step 1, ball rolling behavior: After rolling the feces into a ball, the dung beetle uses celestial clues to navigate along a straight line. If there is no light source, the path will be curved, and natural factors may cause deviation from the direction. The individual position update formula of the ball rolling behavior is: (7); In formula (7), is the deflection coefficient, and , is a constant for adjusting the step size, and , is the natural coefficient, simulating environmental interference, is the current iteration number, is the position information of the i-th dung beetle at the t-th iteration, Used to simulate light intensity changes, and , is the global worst position, used to expand the search range; Step 2, dance behavior: When the dung beetle encounters an obstacle, it will climb onto the dung ball and dance, that is, rotate and pause the dung ball to re-determine the direction. The position update of the dance behavior uses the tangent function: (8); In formula (8), is the position information of the 𝑖th dung beetle at the tth iteration, is the deflection angle, and ; Step 3: Foraging behavior: Dung beetles come out of the ground to look for food. The optimal foraging area will guide the dung beetles to forage. The boundary of the optimal dung beetle area is defined as follows: (9); In formula (9), is the current global best position, , are the next and previous periods of the optimal dung beetle area, respectively. and It is to optimize the lower and upper bounds of the dung beetle area. The dung beetle position is updated as follows: (10); In formula (10), is a random number that follows a normal distribution, is a random vector, and ; Step 4, Stealing behavior: Some dung beetles will steal the dung balls of other dung beetles, which is very common in nature. In the algorithm, it is assumed that around the current global optimal position The area is the best location to compete for food, and the position update formula of the stealing dung beetle is: (11); In formula (11), is a random vector that follows a normal distribution, is a constant that controls the disturbance amplitude, is the current local optimal position; Step 5, Reproduction behavior: Female dung beetles bury dung balls underground to lay eggs. To ensure the safety of their offspring, the selection of the egg-laying area is crucial. The regional boundary selection strategy for simulating the egg-laying of female dung beetles is defined as: (12); In formula (12), is the current local optimal position, , are the lower and upper limits of the spawning area, and is the lower and upper bounds of the optimized dung beetle region, , is the maximum number of iterations, and the position of the egg ball laid by the dung beetle is updated as follows: (13); In formula (13), is the position information of the i-th egg ball at the t-th iteration, and are two independent random vectors.

[0021] Furthermore, in the step 4, MATLAB is used to model and simulate the control of the vehicle-mounted equipment rotation system, which is characterized in that, first, the parameters are set in MATLAB using the improved dung beetle optimization algorithm model to simulate the behavior of dung beetles, handle position crossings, guide theft behavior, optimize the current inner loop PID controller parameters, and find the best parameter combination. Secondly, the permanent magnet synchronous motor control model is modeled in MATLAB as the power core of the system, and the signal control of the current inner loop PID controller is used to observe the change in motor speed and supply energy to the system. Thirdly, the space vector pulse width modulation model receives the current inner loop PID controller signal, converts it into an inverter switching signal in MATLAB, and screens the adaptation scheme to control the motor voltage and current, thereby improving the control accuracy and system stability. Finally, the observer model estimates the unpredictable state variables in MATLAB based on the measurable signal of the motor, and feeds back to the current inner loop PID controller for accurate error calculation and generation of reasonable signals. It also provides accurate motor state information for other models to ensure precise control of the system. These models cooperate with each other, adjust and optimize, simulate the operation of the system under different working conditions, verify the effectiveness of the control method in this paper, and provide theoretical and technical support for practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of the control system based on the vehicle-mounted equipment rotation system.

[0023] Figure 2 This is a model of the control system based on the vehicle-mounted equipment rotation system.

[0024] Figure 3 This is a fitness value comparison chart of the dung beetle optimization algorithm and the improved dung beetle algorithm.

[0025] Figure 4 This is a comparison chart of the effects of the optimized current inner loop PID controller of the dung beetle optimization algorithm and the improved dung beetle algorithm. DETAILED DESCRIPTION

[0026] 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.

[0027] The present invention provides a control method based on a vehicle-mounted equipment rotation system, which is characterized in that the control parameters of the vehicle-mounted equipment rotation system are optimized by improving the dung beetle optimization algorithm, and the specific steps are as follows:

[0028] Step 1: Construct a PID control model based on the vehicle-mounted equipment rotation system, including an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a current inner loop PID controller, a space vector pulse width modulation model, and an observer model.

[0029] Furthermore, in the step one, a PID control model based on the vehicle-mounted equipment rotary system is constructed, including an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a PID controller, a space vector pulse width modulation model, and an observer model, characterized in that the improved dung beetle optimization algorithm model is improved on the basis of the traditional dung beetle optimization algorithm to better adapt to the complex environment and control requirements of the vehicle-mounted equipment rotary system; the permanent magnet synchronous motor control model is the power core of the vehicle-mounted equipment rotary system, and a motor control model is established according to the motion equation of the permanent magnet synchronous motor to observe the changes in the motor speed and position over time. Law, to achieve precise control of the vehicle-mounted equipment rotation system; PID controller is the core link of the entire rotation system control, according to the error between the input and output of the system, through the linear combination of the three links of proportion, integration and differentiation to generate the control signal; the space vector pulse width modulation model is used to convert the control signal output by the PID controller into the switching signal of the inverter, so as to control the stator voltage and current of the permanent magnet synchronous motor, thereby improving the control accuracy and performance of the permanent magnet synchronous motor; the observer model is based on the measurable input and output signals of the motor to estimate the state variables of the motor that are difficult to measure directly or with insufficient measurement accuracy.

[0030] Furthermore, the motion equation of the permanent magnet synchronous motor is an important equation that describes the mechanical motion characteristics of the motor. It establishes the relationship between the motor electromagnetic torque, load torque, the moment of inertia of the motor and load, the mechanical angular velocity and the viscous friction coefficient. The motion equation of the permanent magnet synchronous motor is: (1); In formula (1), is the electromagnetic torque, is the load torque, is the moment of inertia of the motor and load, is the mechanical angular velocity of the motor, is the viscous friction coefficient.

[0031] Furthermore, in the vehicle-mounted equipment slewing system, the PID controller acts in the current inner loop and becomes a current inner loop PID controller. The current inner loop PID controller receives the actual speed signal from the observer, compares it with the target speed, calculates the error, and then calculates the control signal according to the mathematical model of the current inner loop PID controller. The control signal is sent to the drive circuit of the permanent magnet synchronous motor to adjust the input current of the motor, thereby changing the speed of the motor, so that the actual state of the slewing system gradually approaches the desired state. The mathematical model of the current inner loop PID controller is: (2); In formula (2), is the control signal output by the current inner loop PID controller at time t, is the error between the expected input value and the actual output value at time t, , , are the proportional coefficient, integral coefficient and differential coefficient respectively.

[0032] Furthermore, the space vector pulse width modulation model can capture the changes in the output signal of the current inner loop PID controller. Through algorithm logic, it can accurately select the solution that best suits the current control needs from a large number of inverter switch state combinations and respond quickly.

[0033] Furthermore, the observer model focuses on the state variables of the motor that are difficult to measure directly or with poor measurement accuracy. It uses the measurable input and output signals of the motor to carry out estimation work, deeply associates the input voltage and current signals with the output speed and position feedback signals, and keenly captures the dynamic changes of the input and output signals during operation. Through the gain matrix, it continuously compares the difference between the actual output and the estimated output, and uses this to correct the estimated value of the state variable. The mathematical model of the observer is shown as follows: (3); In formula (3), is the estimated state, and , is the estimated output, and , is the gain matrix, and , is the actual output, To estimate the rate of change of state, is the input signal of the motor system, which is generally a measurable speed and position feedback signal. A, B, and C are the system matrix, input matrix, and output matrix, respectively.

[0034] Step 2: Improve the dung beetle optimization algorithm, which is specifically implemented as follows: D1: Use the boundary reflection method to deal with the problem of dung beetle individual position crossing the boundary; D2: Introduce the successful experience of other dung beetles in the stealing process of dung beetles and guide the stealing behavior through the historical optimal position.

[0035] Furthermore, in the step 2, a boundary reflection method is used to handle the problem of individual positions of dung beetles crossing the boundary, characterized in that when the position of an individual dung beetle exceeds the boundary of the search space, the boundary reflection method can be used to effectively pull it back into the legal search space. If the individual position is less than the lower boundary, it is reflected to a symmetrical position on the other side of the lower boundary. If the individual position is greater than the upper boundary, it is reflected to a symmetrical position on the other side of the upper boundary. The boundary reflection method is used to handle the problem of individual positions of dung beetles crossing the boundary, which solves the problems of invalid search, poor stability, and low population diversity caused by individual positions crossing the boundary in the original algorithm, and improves the algorithm performance. The mathematical model of the boundary reflection method is shown in the following formula: (4); In formula (4), is the original position of the i-th individual in the j-th dimension, is the position of the i-th individual in the j-th dimension after boundary reflection, is the lower limit of the search space in the jth dimension, is the upper limit of the search space in the jth dimension.

[0036] Furthermore, in the step 2, the successful experience of other dung beetles is introduced into the stealing process of the dung beetle, and the stealing behavior is guided by the historical optimal position. The characteristic is that it breaks the limitation of individual independent exploration and builds a bridge for group information interaction, so that each dung beetle no longer steals blindly and randomly, but captures potential high-quality solution areas based on the historical optimal positions accumulated by itself and its companions in past iterations, adjusts its own moving direction and distance, and moves closer to the optimal solution more accurately, which greatly improves the search efficiency and enables the algorithm to quickly focus on the global optimal solution. The mathematical model of this process is: (5); In formula (5), is the inertia weight, is the number of other dung beetles that participate in influencing the theft behavior of the i-th dung beetle, is the weight coefficient, is the global historical optimal position, is the position of the i-th dung beetle at the t-th iteration, is the historical optimal position of the j-th dung beetle at the t-th iteration.

[0037] Step three, use the improved dung beetle optimization algorithm to optimize the current inner loop PID controller of the vehicle-mounted equipment rotation system, and obtain the optimal Kp, Ki, and Kd control parameters of the current inner loop PID controller.

[0038] Furthermore, in the step three, the improved dung beetle optimization algorithm is used to optimize the current inner loop PID controller of the vehicle-mounted equipment rotation system to obtain the optimal Kp, Ki, and Kd control parameters of the current inner loop PID controller. It is characterized in that the current inner loop PID controller mainly acts on the permanent magnet synchronous motor control, and then controls the vehicle-mounted equipment rotation system. The improved dung beetle optimization algorithm is used to optimize the parameter setting of the current inner loop PID controller, and the optimal Kp, Ki, and Kd parameters of the current inner loop PID controller are obtained by iteration, thereby optimizing the control performance of the current inner loop PID controller. The specific steps are: S1. Parameter initialization: Set the maximum number of iterations of the improved dung beetle optimization algorithm and population size , population dimension dim, search upper bound ub, lower bound lb, the initial position of the improved dung beetle optimization algorithm is the initial solution of the current inner loop PID parameters in the algorithm optimization process, and the position update of the dung beetle is the parameter update of the current inner loop PID controller; S2. Setting of the current inner loop PID parameters: By improving the dung beetle optimization algorithm, the Kp, Ki, and Kd parameter combination that achieves the best balance between the system dynamic response and steady-state performance is selected. Due to the time-sensitive characteristics of the improved dung beetle optimization algorithm, an indicator that can quantify the time cumulative error is required as the fitness function. The time multiplied by the absolute error integral can not only punish overshoot and oscillation due to its time-domain weighted characteristics of the error, but also effectively reflect the rapidity and steady-state accuracy of the system, thereby more accurately evaluating the comprehensive performance of the PID parameters. Its mathematical expression formula is as follows: (6); In formula (6), is a key indicator for measuring the deviation between the actual current and the expected reference current, and T is the cumulative effect used to integrate the error; S3, simulate the ball rolling and foraging behaviors of dung beetles: by simulating the five behavioral stages of ball rolling, dancing, foraging, stealing and reproduction of dung beetles, the position of dung beetle populations is dynamically adjusted to gradually approach the optimal solution; S4. Start iteration: Execute In each iteration optimization process, the current fitness value is compared with the historical optimal fitness value to determine whether to update the optimal fitness value. At the same time, the optimal solution of the group is updated according to the fitness value of each dung beetle individual. S5. Judgment: Determine whether the number of iterations t has reached the maximum number of iterations If it is reached, stop and output the optimal solution. If it is not reached, continue to execute steps S3 and S4; S6, assignment: assign the optimal solution to the three parameters Kp, Ki, and Kd of the current inner loop PID controller to complete the parameter optimization of the current inner loop PID controller.

[0039] Furthermore, in said S3, the ball rolling and foraging behaviors of dung beetles are simulated: by simulating the five behavioral stages of ball rolling, dancing, foraging, stealing and reproduction of dung beetles, the position of the dung beetle population is dynamically adjusted to gradually approach the optimal solution, which is characterized by integrating a unique mechanism of multiple behavioral stages, so that the algorithm exhibits efficient and flexible search capabilities in the search space: Step 1, ball rolling behavior: After rolling the feces into a ball, the dung beetle uses celestial clues to navigate along a straight line. If there is no light source, the path will be curved, and natural factors may cause deviation from the direction. The individual position update formula of the ball rolling behavior is: (7); In formula (7), is the deflection coefficient, and , is a constant for adjusting the step size, and , is the natural coefficient, simulating environmental interference, is the current iteration number, is the position information of the i-th dung beetle at the t-th iteration, Used to simulate light intensity changes, and , is the global worst position, used to expand the search range; Step 2, dance behavior: When the dung beetle encounters an obstacle, it will climb onto the dung ball and dance, that is, rotate and pause the dung ball to re-determine the direction. The position update of the dance behavior uses the tangent function: (8); In formula (8), is the position information of the 𝑖th dung beetle at the tth iteration, is the deflection angle, and ; Step 3: Foraging behavior: Dung beetles come out of the ground to look for food. The optimal foraging area will guide the dung beetles to forage. The boundary of the optimal dung beetle area is defined as follows: (9); In formula (9), is the current global best position, , are the next and previous periods of the optimal dung beetle area, respectively. and It is to optimize the lower and upper bounds of the dung beetle area. The dung beetle position is updated as follows: (10); In formula (10), is a random number that follows a normal distribution, is a random vector, and ; Step 4, Stealing behavior: Some dung beetles will steal the dung balls of other dung beetles, which is very common in nature. In the algorithm, it is assumed that around the current global optimal position The area is the best location to compete for food, and the position update formula of the stealing dung beetle is: (11); In formula (11), is a random vector that follows a normal distribution, is a constant that controls the disturbance amplitude, is the current local optimal position; Step 5, Reproduction behavior: Female dung beetles bury dung balls underground to lay eggs. To ensure the safety of their offspring, the selection of the egg-laying area is crucial. The regional boundary selection strategy for simulating the egg-laying of female dung beetles is defined as: (12); In formula (12), is the current local optimal position, , are the lower and upper limits of the spawning area, and is the lower and upper bounds of the optimized dung beetle region, , is the maximum number of iterations, and the position of the egg ball laid by the dung beetle is updated as follows: (13); In formula (13), is the position information of the i-th egg ball at the t-th iteration, and are two independent random vectors.

[0040] Step 4: Use MATLAB to model and simulate the control of the vehicle-mounted equipment rotation system.

[0041] Furthermore, in the step 4, MATLAB is used to model and simulate the control of the vehicle-mounted equipment rotation system, which is characterized in that, first, the parameters are set in MATLAB using the improved dung beetle optimization algorithm model to simulate the behavior of dung beetles, handle position crossings, guide theft behavior, optimize the current inner loop PID controller parameters, and find the best parameter combination. Secondly, the permanent magnet synchronous motor control model is modeled in MATLAB as the power core of the system, and the signal control of the current inner loop PID controller is used to observe the change in motor speed and supply energy to the system. Thirdly, the space vector pulse width modulation model receives the current inner loop PID controller signal, converts it into an inverter switching signal in MATLAB, and screens the adaptation scheme to control the motor voltage and current, thereby improving the control accuracy and system stability. Finally, the observer model estimates the unpredictable state variables in MATLAB based on the measurable signal of the motor, and feeds back to the current inner loop PID controller for accurate error calculation and generation of reasonable signals. It also provides accurate motor state information for other models to ensure precise control of the system. These models cooperate with each other, adjust and optimize, simulate the operation of the system under different working conditions, verify the effectiveness of the control method in this paper, and provide theoretical and technical support for practical applications.

[0042] Figure 3 This is a fitness function comparison curve of the dung beetle optimization algorithm and the improved dung beetle optimization algorithm. At the beginning of the iteration, the fitness value of the improved dung beetle optimization algorithm is continuously smaller than that of the dung beetle optimization algorithm. It can be proved that the improved dung beetle optimization algorithm obtains the optimal fitness value faster than the dung beetle optimization algorithm. According to the principle that the smaller the fitness value, the better the solution, the optimal solution obtained by the improved dung beetle optimization algorithm is better than that of the dung beetle optimization algorithm.

[0043] Figure 4 This is a comparison chart of the PID responses of the dung beetle optimization algorithm and the improved dung beetle optimization algorithm. The PID response curves of the two algorithms are compared by setting the target value 1. It can be seen from the figure that under the condition of tending to the same target value 1, the improved dung beetle optimization algorithm takes less time and has a smaller fluctuation range of the vertical axis than the dung beetle optimization algorithm. This shows that in a complex environment, the PID controller system based on the improved dung beetle optimization algorithm has better performance than the PID controller system based on the dung beetle optimization algorithm.

Claims

1. A control method based on a vehicle-mounted equipment rotation system, characterized in that: The control parameters of the vehicle-mounted equipment rotation system are optimized by improving the dung beetle optimization algorithm. The specific steps are as follows: Step 1: construct a PID control model based on the vehicle-mounted equipment slewing system, including an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a current inner loop PID controller, a space vector pulse width modulation model, and an observer model; Step 2: Improve the optimization algorithm of dung beetles, which is specifically implemented as follows: D1: Use the boundary reflection method to deal with the problem of dung beetle individual position crossing the boundary; D2: Introduce the successful experience of other dung beetles in the stealing process of dung beetles, and guide the stealing behavior through the historical optimal position; Step 3, using the improved dung beetle optimization algorithm to optimize the current inner loop PID controller of the vehicle-mounted equipment slewing system, and obtaining the optimal Kp, Ki, and Kd control parameters of the current inner loop PID controller; Step 4: Use MATLAB to model and simulate the control of the vehicle-mounted equipment rotation system.

2. A control method based on a vehicle-mounted equipment rotation system according to claim 1, characterized in that: In the step 1, a PID control model based on the vehicle-mounted equipment rotation system is constructed, including an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a current inner loop PID controller, a space vector pulse width modulation model, and an observer model; the improved dung beetle optimization algorithm model is improved on the basis of the traditional dung beetle optimization algorithm to better adapt to the complex environment and control requirements of the vehicle-mounted equipment rotation system; the permanent magnet synchronous motor control model is the power core of the vehicle-mounted equipment rotation system. According to the motion equation of the permanent magnet synchronous motor, a motor control model is established to observe the change law of the motor speed and position over time to achieve precise control of the vehicle-mounted equipment rotation system; the current inner loop PID controller is the core link of the entire rotation system control, and a control signal is generated through a linear combination of the three links of proportion, integration and differentiation according to the error between the input and output of the system; the space vector pulse width modulation model is used to convert the control signal output by the current inner loop PID controller into a switching signal of the inverter, thereby controlling the stator voltage and current of the permanent magnet synchronous motor; the observer model estimates the state variables of the motor that are difficult to measure directly or the measurement accuracy is not high enough according to the measurable input and output signals of the motor.

3. A control method based on a vehicle-mounted equipment rotation system according to claim 1, characterized in that: In the step 2, the dung beetle optimization algorithm is improved, which is divided into two parts of improvement, which are specifically implemented as follows: D1. Use the boundary reflection method to deal with the problem of dung beetle individual position crossing the boundary. When the position of the dung beetle individual exceeds the boundary of the search space, the boundary reflection method can effectively pull it back to the legal search space. If the individual position is less than the lower boundary, it is reflected to the symmetrical position on the other side of the lower boundary. If the individual position is greater than the upper boundary, it is reflected to the symmetrical position on the other side of the upper boundary. The boundary reflection method is used to deal with the problem of dung beetle individual position crossing the boundary. The mathematical model of the boundary reflection method is shown in the following formula: （4）； In formula (4), is the original position of the i-th individual in the j-th dimension, is the position of the i-th individual in the j-th dimension after boundary reflection, is the lower limit of the search space in the jth dimension, is the upper limit of the search space in the jth dimension; D2. In the process of stealing, the successful experience of other dung beetles is introduced to guide the stealing behavior through the historical optimal position, breaking the limitation of individual independent exploration and building a bridge for group information interaction. Each dung beetle no longer steals blindly and randomly, but captures potential high-quality solution areas based on the historical optimal positions accumulated by itself and its companions in past iterations, adjusts its own moving direction and distance, and moves closer to the optimal solution more accurately, greatly improving the search efficiency and enabling the algorithm to quickly focus on the global optimal solution. The mathematical model of this process is: （5）； In formula (5), is the inertia weight, is the number of other dung beetles that participate in influencing the theft behavior of the i-th dung beetle, is the weight coefficient, is the global historical optimal position, is the position of the i-th dung beetle at the t-th iteration, is the historical optimal position of the j-th dung beetle at the t-th iteration.

4. The control method based on the vehicle-mounted equipment rotation system according to claim 1, characterized in that: In the step 3, the improved dung beetle optimization algorithm is used to optimize the current inner loop PID controller of the permanent magnet synchronous motor control system to obtain the optimal Kp, Ki, and Kd control parameters of the current inner loop PID controller. The specific steps are: S1. Parameter initialization: Set the maximum number of iterations of the improved dung beetle optimization algorithm and population size , population dimension dim, search upper bound ub, lower bound lb, the initial position of the improved dung beetle optimization algorithm is the initial solution of the current inner loop PID parameters in the algorithm optimization process, and the position update of the dung beetle is the parameter update of the current inner loop PID controller; S2. Setting of the current inner loop PID parameters: By improving the dung beetle optimization algorithm, the Kp, Ki, and Kd parameter combination that achieves the best balance between the system dynamic response and steady-state performance is selected. Due to the time-sensitive characteristics of the improved dung beetle optimization algorithm, an indicator that can quantify the time cumulative error is required as the fitness function. The time multiplied by the absolute error integral can not only punish overshoot and oscillation due to its time-domain weighted characteristics of the error, but also effectively reflect the rapidity and steady-state accuracy of the system, thereby more accurately evaluating the comprehensive performance of the PID parameters. Its mathematical expression formula is as follows: （6）； In formula (6), is a key indicator for measuring the deviation between the actual current and the expected reference current, and T is the cumulative effect used to integrate the error; S3, simulate the ball rolling and foraging behaviors of dung beetles: by simulating the five behavioral stages of ball rolling, dancing, foraging, stealing and reproduction of dung beetles, the position of dung beetle populations is dynamically adjusted to gradually approach the optimal solution; S4. Start iteration: Execute In each iteration optimization process, the current fitness value is compared with the historical optimal fitness value to determine whether to update the optimal fitness value. At the same time, the optimal solution of the group is updated according to the fitness value of each dung beetle individual. S5. Judgment: Determine whether the number of iterations t has reached the maximum number of iterations If it is reached, stop and output the optimal solution. If it is not reached, continue to execute steps S3 and S4; S6, assignment: assign the optimal solution to the three parameters Kp, Ki, and Kd of the current inner loop PID controller to complete the parameter optimization of the current inner loop PID controller.

5. The control method based on the vehicle-mounted equipment rotation system according to claim 1, characterized in that: In the step 4, MATLAB is used to model and simulate the control of the vehicle-mounted equipment rotation system. First, the improved dung beetle optimization algorithm model is used to set parameters in MATLAB, simulate the behavior of dung beetles, handle position violations, guide theft behavior, optimize the current inner loop PID controller parameters, and find the best parameter combination. Secondly, the permanent magnet synchronous motor control model is modeled in MATLAB as the system power core, and the signal control of the current inner loop PID controller is used to observe the change in motor speed and supply energy to the system. Thirdly, the space vector pulse width modulation model receives the current inner loop PID controller signal, converts it into an inverter switching signal in MATLAB, and selects the adaptation scheme to control the motor voltage and current, thereby improving the control accuracy and system stability. Finally, the observer model estimates the unpredictable state variables in MATLAB based on the measurable signals of the motor, and feeds back to the current inner loop PID controller for accurate error calculation and generation of reasonable signals.

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