Active disturbance rejection control method for automobile durability test platform based on dung beetle optimization algorithm

Abstract

The present invention belongs to the field of automotive test platform control technology and provides an auto-disturbance rejection control method for an automotive durability test platform based on a dung beetle optimization algorithm. The method comprises the following steps: S1, establishing a drive control system model for the automotive durability test platform; S2, designing an auto-disturbance rejection controller based on the control system model; S3, using the dung beetle optimization algorithm to self-tune parameters to be tuned in the auto-disturbance rejection controller; and S4, integrating Kent's chaos theory and a differential evolution algorithm into the dung beetle optimization algorithm and dynamically adjusting the iterative parameters. The method estimates and compensates for internal and external disturbances in the system through a tracking differentiator, a nonlinear state error feedback control law, and an extended state observer in the auto-disturbance rejection controller, thereby improving the response speed and accuracy of the control system. The method also integrates Kent's chaotic mapping and a differential evolution algorithm into the dung beetle optimization algorithm to nonlinearly optimize the iterative parameters of the dung beetle optimization algorithm, thereby enhancing the control efficiency and robustness of the system in dynamic response.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile test platform control, and in particular relates to an auto-disturbance rejection control method for an automobile durability test platform based on a dung beetle optimization algorithm. Background Art

[0002] To ensure the stability and reliability of automobile performance, durability testing is essential. The purpose of a durability test platform is to simulate various operating conditions of a car during actual driving in order to evaluate its performance after long-term use, including the stability of the power system, the wear of various components, and the coordination of the overall system. Road spectrum reproduction accuracy is the core indicator for evaluating the performance of a car's durability test platform. Its level largely determines the quality of the car's fatigue durability performance. In recent years, servo control technology has been increasingly used in mechanical systems. Its high precision and high responsiveness make it an ideal choice for durability test platforms. Through the drive control system, the motion state of the test platform can be precisely controlled, enabling it to simulate various dynamic conditions during actual driving, such as acceleration, braking, and climbing.

[0003] Currently, automotive durability test platforms primarily rely on traditional PID control strategies, which perform reliably in most situations. However, they often struggle in complex dynamic test environments due to their inability to handle noise and dynamic disturbances. Active disturbance rejection controllers (ADRCs), with their high noise immunity and robustness, can estimate and compensate for internal and external disturbances to enhance system performance and are widely used in servo control scenarios. The complexity of parameter adjustment in ADRCs makes it difficult to effectively achieve optimal control performance, resulting in low control accuracy and slow response speeds in existing durability test platforms. Therefore, it is necessary to propose an ADRC control method for automotive durability test platforms based on the dung beetle optimization algorithm. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides an auto-disturbance rejection control method for an automobile durability test platform based on a dung beetle optimization algorithm. The method estimates and compensates for internal and external disturbances of the system through a tracking differentiator, a nonlinear state error feedback control law, and an extended state observer in the auto-disturbance rejection controller, thereby improving the response speed and control accuracy of the control system. By integrating Kent chaos mapping and a differential evolution algorithm into the dung beetle optimization algorithm and performing nonlinear improvement on the iterative parameters of the dung beetle optimization algorithm, the tuning efficiency and accuracy of the auto-disturbance rejection controller parameters are improved, thereby enhancing the control efficiency and robustness of the system during the dynamic response process.

[0005] The present invention provides an auto-disturbance rejection control method for an automobile durability test platform based on a dung beetle optimization algorithm, which comprises the following steps:

[0006] S1. Establish a drive control system model for the vehicle durability test platform;

[0007] S2. Design an active disturbance rejection controller based on the drive control system model, which includes a tracking controller, an extended state observer, and a nonlinear state error feedback control law;

[0008] S3. Use the improved dung beetle optimization algorithm to self-tune multiple parameters to be adjusted in the active disturbance rejection controller. Use the time-weighted absolute error integral of the ball screw to construct the fitness function of the active disturbance rejection controller. The parameters to be adjusted include 5, which are the gain coefficient β in the extended state observer. 01 , β 02 , β 03 and the system gain coefficients β1 and β2 in the nonlinear state error feedback control law, relating the parameters to be tuned to the dung beetle positions, initializing the population and setting the fitness function;

[0009] S4. Integrate Kent's chaos theory and differential evolution algorithm into the dung beetle optimization algorithm, dynamically adjust the iterative parameters of the stealing dung beetle in the dung beetle optimization algorithm through nonlinearity, and dynamically update the optimized parameters to the extended state observer and nonlinear state error feedback control law of the active disturbance rejection controller to realize the active disturbance rejection control of the vehicle test platform motion. The sub-steps include:

[0010] S41. The dung beetle optimization algorithm based on Kent chaos mapping is used to initialize the population. The expression of Kent chaos mapping is:

[0011]

[0012] Bringing the Kent chaotic map into the population initialization of the dung beetle optimization algorithm, its expression is:

[0013] x i =Lb+(Ub-Lb)×y i

[0014] Where a is the control parameter, x i is the initial position of the dung beetle, y i is the kent chaotic sequence, Lb and Ub are the upper and lower bounds of the initialization position of the dung beetle population respectively;

[0015] Optimize the weight vector g in the position update of the stealing dung beetle;

[0016]

[0017] The location of the stealing dung beetle has been updated to:

[0018]

[0019] Where, fit best is the optimal fitness value, fitworst is the worst fitness value, T is the maximum number of iterations, η is the adjustment parameter of the exponential change rate, I is a 1×D vector with all elements equal to 1, g is a 1×D weight vector that obeys a normal distribution, and S is a constant;

[0020] By dynamically adjusting the weight vector g, the global search is balanced to enable the dung beetle optimization algorithm to escape from the local optimum.

[0021] S42. Integrate the differential evolution algorithm into the dung beetle optimization algorithm, calculate the difference between the dung beetles and perform mutation operation with the global optimal dung beetle to generate a new dung beetle. The position of the new dung beetle is:

[0022]

[0023] Where x new t is the newly generated dung beetle, λ is the scaling factor, x r1 and x r2 is a randomly selected dung beetle location.

[0024] Preferably, step S1 includes the following sub-steps:

[0025] S11. Construct a permanent magnet synchronous system model, ignoring minor factors such as motor damping, eddy current hysteresis energy loss, etc. In the dq coordinate system, the decoupling state equation of the permanent magnet synchronous servo system is:

[0026]

[0027] Where i q is the q-axis current, i d is the d-axis current, u q is the q-axis voltage, u d is the d-axis voltage, L d is the equivalent d-axis inductance, L q is the equivalent q-axis inductance, R is the equivalent resistance of the servo motor winding, P n is the number of magnetic pole pairs, J is the moment of inertia, is the equivalent flux linkage of the rotor magnetic field;

[0028] S12, make i d =0 vector control, we get:

[0029]

[0030] Where, d-axis voltage u d is the input quantity of the permanent magnet synchronous servo motor, the rotor angular velocity w r is the output of the permanent magnet synchronous servo motor;

[0031] S13. Obtain the transfer function of each servo drive control system through Laplace transform:

[0032]

[0033] Where, is the torque coefficient, is the position transfer function of the ball screw.

[0034] Preferably, the discrete form of the tracking differentiator constructed in step S2 is:

[0035]

[0036] Where v is the desired linear velocity of the ball screw, q1 is the tracking value of the desired linear velocity v, q2 is the differential of the desired linear velocity v, and fhan() is the fastest integrated function, which is expressed as follows:

[0037]

[0038] Where h is the sampling step, r is the speed factor, q1 and q2 are the state variables of the controlled system, d and s are a 、s y It is the derivation of the function fhan().

[0039] Preferably, in step S2, the nonlinear extended state observer observes the actual linear velocity and interference of the ball screw and compensates for the interference, specifically:

[0040]

[0041] Where, β 01 , β 02 , β 03 is the control parameter, and its value affects the observation performance of the extended state observer. y is the actual linear drive speed of the ball screw, z1 is the estimated value of the actual linear drive speed of the ball screw, z2 is the estimated value of the differential of the actual linear drive speed, and z3 is the estimated value of the total disturbance of the system.

[0042] The nonlinear controller performs nonlinear combination on each link of the error signal. The expression of the nonlinear state error feedback control law is:

[0043]

[0044] Where e1 and e2 are the differences between the transition value and the observed value, u0 is the initial control variable, z1 is the observed displacement, z2 is the observed velocity, β1 and β2 are system gains, α1 ranges from [0, 1], α2>1, and fal(e, α, δ) is a nonlinear saturation function, expressed as:

[0045]

[0046] Where ε is the error signal, a is the controller parameter, and δ is the width of the nonlinear interval.

[0047] Preferably, in step S3, when the dung beetle rolls the dung ball without any obstacles, the position is updated as follows:

[0048]

[0049] When a dung beetle rolls a dung ball in the presence of an obstacle, its position is updated to:

[0050]

[0051] Where t is the number of iterations, is the position of the i-th dung beetle in the population at the t-th iteration, k∈(0,0.2] is the deflection coefficient, b is a constant between (0,1), φ is a natural number assigned -1 or 1, 1 means no deviation, -1 means deviation from the direction, The worst position in the population.

[0052] Preferably, the position of the dung beetle embryo in step S3 is updated as follows:

[0053]

[0054] Where B i t+1 is the position of the i-th chick at the t-th update, b1 and b2 are 1×D independent random vectors, Ub* and Lb* are the upper and lower bounds of the chick position, respectively, specifically:

[0055]

[0056] Preferably, the foraging position of the small dung beetle in step S3 is updated to:

[0057]

[0058] Where C1 is a random number that obeys the normal distribution, that is, C1~N(0,1), C1 is a 1×D random vector between (0,1), Lb t and Ub t The upper and lower bounds of the dung beetle's foraging area are:

[0059]

[0060] Preferably, the input end of the drive control system receives the linear motion signal and the actual linear motion signal, and converts the signal into the expected servo motor speed signal w* and the actual servo motor speed signal w according to the transmission ratio, and outputs the voltage control signal u through the improved active disturbance rejection controller. qAs for the permanent magnet synchronous servo motor, the rotational motion of the permanent magnet synchronous servo motor is converted into linear motion through a ball screw, and the self-disturbance rejection control of the test platform is realized by improving the dung beetle optimization algorithm.

[0061] Compared with the prior art, the present invention has the following advantages:

[0062] This paper presents an active disturbance rejection control method for an automotive durability test platform based on the dung beetle optimization algorithm. By introducing an active disturbance rejection controller and utilizing a tracking differentiator, a nonlinear state error feedback control law, and an extended state observer to estimate and compensate for internal and external disturbances, the control system's response speed and accuracy are improved. By integrating Kent's chaotic mapping with the differential evolution algorithm and performing nonlinear optimization on the algorithm's iterative parameters, the tuning efficiency and accuracy of the active disturbance rejection controller's parameters are enhanced, effectively improving the control efficiency and robustness of the system during dynamic response. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of the auto-disturbance rejection control method of the automobile durability test platform based on the dung beetle optimization algorithm of the present invention;

[0064] Figure 2 Schematic diagram of transfer functions of various drive control systems of the automobile durability test platform of the present invention;

[0065] Figure 3 This is a schematic diagram of a nonlinear active disturbance rejection controller that improves the dung beetle optimization algorithm in the active disturbance rejection control method of the automobile durability test platform of the present invention;

[0066] Figure 4 A tracking curve diagram of the expected displacement of each drive control system model of the automobile durability test platform of the present invention;

[0067] Figure 5 This is a tracking error diagram of the expected displacement of the mathematical models of each drive control system of the automobile durability test platform of the present invention. DETAILED DESCRIPTION

[0068] To fully describe the technical content, structural features, objectives and effects of the present invention, the following is a detailed description with reference to the accompanying drawings.

[0069] The present invention is based on the dung beetle optimization algorithm of the automobile durability test platform self-disturbance rejection control method, such as Figure 1 As shown, it includes the following steps:

[0070] S1. Establish a drive control system model for the vehicle durability test platform, including the following sub-steps:

[0071] S11. Construct a permanent magnet synchronous system model, ignoring minor factors such as motor damping, eddy current hysteresis energy loss, etc. In the dq coordinate system, the decoupling state equation of the permanent magnet synchronous servo system is:

[0072]

[0073] Where i q is the q-axis current, i d is the d-axis current, u q is the q-axis voltage, u d is the d-axis voltage, L d is the equivalent d-axis inductance, L q is the equivalent q-axis inductance, R is the equivalent resistance of the servo motor winding, P n is the number of magnetic pole pairs, J is the moment of inertia, is the equivalent magnetic flux of the rotor magnetic field.

[0074] S12, make i d =0 vector control, we get:

[0075]

[0076] Where, d-axis voltage u d is the input quantity of the permanent magnet synchronous servo motor, the rotor angular velocity w r is the output of the permanent magnet synchronous servo motor.

[0077] S13. Obtain the transfer function of each servo drive control system through Laplace transform:

[0078]

[0079] Where, is the torque coefficient, is the position transfer function of the ball screw.

[0080] S2. Based on the drive control system model, an active disturbance rejection controller is designed, which includes a tracking controller, an extended state observer, and a nonlinear state error feedback control law, such as Figure 2 shown.

[0081] In step S2, the discrete form of the tracking differentiator is constructed, specifically:

[0082]

[0083] Where v is the desired linear velocity of the ball screw, q1 is the tracking value of the desired linear velocity v, q2 is the differential of the desired linear velocity v, and fhan() is the fastest integrated function, which is expressed as follows:

[0084]

[0085] Where h is the sampling step, r is the speed factor, q1 and q2 are the state variables of the controlled system, d and s are a 、s y It is the derivation of the function fhan().

[0086] In step S2, the nonlinear extended state observer observes the actual linear velocity and interference of the ball screw and compensates for the interference, specifically:

[0087]

[0088] Where, β 01 , β 02 , β 03 is the control parameter, and its value affects the observation performance of the extended state observer. y is the actual linear drive speed of the ball screw, z1 is the estimated value of the actual linear drive speed of the ball screw, z2 is the estimated value of the differential of the actual linear drive speed, and z3 is the estimated value of the total disturbance of the system.

[0089] The nonlinear controller performs nonlinear combination on each link of the error signal. The expression of the nonlinear state error feedback control law is:

[0090]

[0091] Where e1 and e2 are the differences between the transition value and the observed value, u0 is the initial control variable, z1 is the observed displacement, z2 is the observed velocity, β1 and β2 are system gains, α1 ranges from [0, 1], α2>1, and fal(e, α, δ) is a nonlinear saturation function, expressed as:

[0092]

[0093] Where ε is the error signal, a is the controller parameter, and δ is the width of the nonlinear interval.

[0094] S3. Use the improved dung beetle optimization algorithm to self-tune multiple parameters to be adjusted in the active disturbance rejection controller. Use the time-weighted absolute error integral of the ball screw to construct the fitness function of the active disturbance rejection controller. The parameters to be adjusted include 5, which are the gain coefficient β in the extended state observer. 01 , β 02 , β 03 and the system gain coefficients β1 and β2 in the nonlinear state error feedback control law, relate the parameters to be tuned to the dung beetle position, initialize the population and set the fitness function.

[0095] In step S3, the position of the dung beetle when rolling the dung ball without any obstacles is updated as follows:

[0096]

[0097] When a dung beetle rolls a dung ball in the presence of an obstacle, its position is updated to:

[0098]

[0099] Where t is the number of iterations, is the position of the i-th dung beetle in the population at the t-th iteration, k∈(0,0.2] is the deflection coefficient, b is a constant between (0,1), φ is a natural number assigned -1 or 1, 1 means no deviation, -1 means deviation from the direction, The worst position in the population.

[0100] The position of the dung beetle embryo ball in step S3 is updated as follows:

[0101]

[0102] Where B i t+1 is the position of the i-th chick at the t-th update, b1 and b2 are 1×D independent random vectors, Ub* and Lb* are the upper and lower bounds of the chick position, respectively, specifically:

[0103]

[0104] The foraging position of the small dung beetle in step S3 is updated to:

[0105]

[0106] Where C1 is a random number that obeys the normal distribution, that is, C1~N(0,1), C1 is a 1×D random vector between (0,1), Lb t and Ub t The upper and lower bounds of the dung beetle's foraging area are:

[0107]

[0108] S4. Integrate Kent's chaos theory and differential evolution algorithm into the dung beetle optimization algorithm, dynamically adjust the iterative parameters of the stealing dung beetle in the dung beetle optimization algorithm through nonlinearity, and dynamically update the optimized parameters to the extended state observer and nonlinear state error feedback control law of the active disturbance rejection controller to realize the active disturbance rejection control of the vehicle test platform motion. The sub-steps include:

[0109] S41. The dung beetle optimization algorithm based on Kent chaos mapping is used to initialize the population. The expression of Kent chaos mapping is:

[0110]

[0111] Bringing the Kent chaotic map into the population initialization of the dung beetle optimization algorithm, its expression is:

[0112] x i =Lb+(Ub-Lb)×y i

[0113] In the formula, a is the control parameter, which is 0.4, x i is the initial position of the dung beetle, y i is the kent chaotic sequence, Lb and Ub are the upper and lower bounds of the initialization position of the dung beetle population respectively;

[0114] Optimize the weight vector g in the position update of the stealing dung beetle;

[0115]

[0116] The location of the stealing dung beetle has been updated to:

[0117]

[0118] Where, fit best is the optimal fitness value, fit worst is the worst fitness value, T is the maximum number of iterations, η is the adjustment parameter of the exponential change rate, I is a 1×D vector with all elements equal to 1, g is a 1×D weight vector that obeys the normal distribution, and S is a constant.

[0119] By dynamically adjusting the weight vector g, the ability of the dung beetle optimization algorithm to escape from local optimality and the ability to search globally are balanced.

[0120] S42. Integrate the differential evolution algorithm into the dung beetle optimization algorithm, calculate the difference between the dung beetles and perform mutation operation with the global optimal dung beetle to generate a new dung beetle. The position of the new dung beetle is:

[0121]

[0122] Where x new t is the newly generated dung beetle, λ is the scaling factor, x r1 and x r2 is a randomly selected dung beetle location.

[0123] The input end of the vehicle durability test platform drive control system receives the linear motion signal and the actual linear motion signal, and converts the signal into the expected servo motor speed signal w* and the actual servo motor speed signal w according to the transmission ratio. The improved active disturbance rejection controller outputs the voltage control signal u q As for the permanent magnet synchronous servo motor, the rotational motion of the permanent magnet synchronous servo motor is converted into linear motion through a ball screw, and the self-disturbance rejection control of the test platform is realized by improving the dung beetle optimization algorithm.

[0124] The following is a further description of the active disturbance rejection control method for the automobile durability test platform based on the dung beetle optimization algorithm of the present invention in conjunction with the embodiments:

[0125] The parameters of the driving control system of the vehicle durability test platform in this embodiment are:

[0126] The motor armature inductance is 0.005H, the motor armature resistance is 1.3Ω, the number of motor pole pairs is 1, and the motor rotor and screw moment of inertia is 0.00007kgm 2 , the motor torque coefficient is 1.5, and the ball screw lead is 10mm.

[0127] like Figure 3 As shown in Figure 2, a nonlinear active disturbance rejection controller is designed according to the controlled object, specifically:

[0128] In this embodiment, the improved dung beetle optimization algorithm is used to improve the gain coefficient β in the extended state observer in the active disturbance rejection controller. 01 , β 02 , β 03 The system gain coefficients β1 and β2 in the nonlinear state error feedback control law are taken as parameters to be tuned. The specific values after optimization are: the gain coefficient β in the extended state observer 01 =58,β 02 =1000,β 03 =145, the system gain coefficients in the nonlinear state error feedback control law are β1=1000, β2=145, and the other parameters are a1=0.5, a2=1.25, and δ=0.05.

[0129] like Figure 4 and Figure 5 As shown, the nonlinear active disturbance rejection controller in the active disturbance rejection control method of the automobile durability test platform based on the dung beetle optimization algorithm of the present invention can achieve a good tracking effect on the expected motion trajectory of the servo branch mathematical model, with a very small tracking error and good dynamic performance.

[0130] The present invention discloses an auto-disturbance rejection control method for an automobile durability test platform based on a dung beetle optimization algorithm. To address the difficulty of tuning the parameters of the auto-disturbance rejection controller, the dung beetle optimization algorithm is introduced to find the optimal parameters of the auto-disturbance rejection controller. To address the shortcomings of the dung beetle optimization algorithm, such as its parameter sensitivity and pseudo-randomness, the Kent chaotic mapping is introduced to initialize the dung beetle optimization algorithm population. To enhance the dung beetle optimization algorithm's local search capabilities in the early stages of iteration and its global search capabilities in the later stages of iteration, the algorithm's iterative parameters are dynamically adjusted using a nonlinear method. Combined with a differential evolution algorithm, this method enhances the dung beetle optimization algorithm's ability to escape local optima and improves parameter tuning efficiency. The method improves the control accuracy of the servo control branch of the automobile durability test platform, thereby improving the terminal accuracy of the platform and the road spectrum reproduction rate.

[0131] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. An auto-disturbance rejection control method for an automobile durability test platform based on a dung beetle optimization algorithm, characterized in that: It includes the following steps: S1. Establish a drive control system model for the vehicle durability test platform; S2. Design an active disturbance rejection controller based on the drive control system model, which includes a tracking controller, an extended state observer, and a nonlinear state error feedback control law; Construct the discrete form of the tracking differentiator, specifically: Where v is the desired linear velocity of the ball screw, q1 is the tracking value of the desired linear velocity v, q2 is the differential of the desired linear velocity v, and fhan() is the fastest integrated function, which is expressed as follows: Where h is the sampling step, r is the speed factor, q1 and q2 are the state variables of the controlled system, d and s are a 、s y is the derivation of the function fhan(); The nonlinear extended state observer observes the actual linear velocity and disturbance of the ball screw and compensates for the disturbance, specifically: Where, β 01 , β 02 , β 03 is the control parameter, and its value affects the observation performance of the extended state observer. y is the actual linear drive speed of the ball screw, z1 is the estimated value of the actual linear drive speed of the ball screw, z2 is the estimated value of the differential of the actual linear drive speed, and z3 is the estimated value of the total disturbance of the system. The nonlinear controller performs nonlinear combination on each link of the error signal. The expression of the nonlinear state error feedback control law is: Where e1 and e2 are the differences between the transition value and the observed value, u0 is the initial control variable, z1 is the observed displacement, z2 is the observed velocity, β1 and β2 are system gains, α1 ranges from [0, 1], α2>1, and fal(e, α, δ) is a nonlinear saturation function, expressed as: Where ε is the error signal, a is the controller parameter, and δ is the width of the nonlinear interval; S3. Use the improved dung beetle optimization algorithm to self-tune multiple parameters to be adjusted in the ADRC. Use the time-weighted absolute error integral of the ball screw to construct the fitness function of the ADRC. The parameters to be adjusted include the gain coefficient β in the extended state observer. 01 , β 02 , β 03 and the system gain coefficients β1 and β2 in the nonlinear state error feedback control law, relating the parameters to be tuned to the dung beetle positions, initializing the population and setting the fitness function; S4. Integrate Kent's chaos theory and the differential evolution algorithm into the dung beetle optimization algorithm, dynamically adjust the iterative parameters of the thieving dung beetle in the dung beetle optimization algorithm through nonlinearity, and dynamically update the optimized parameters to the extended state observer and nonlinear state error feedback control law of the active disturbance rejection controller to realize the active disturbance rejection control of the vehicle test platform motion. The sub-steps include: S41. The dung beetle optimization algorithm based on Kent chaos mapping is used to initialize the population. The expression of Kent chaos mapping is: Bringing the Kent chaotic map into the population initialization of the dung beetle optimization algorithm, its expression is: x i =Lb+(Ub-Lb)×y i Where a is the control parameter, x i is the initial position of the dung beetle, y i is the kent chaotic sequence, Lb and Ub are the upper and lower bounds of the initialization position of the dung beetle population respectively; Optimize the weight vector g in the thieving dung beetle's position update: The location of the thieving dung beetle has been updated to: Where, fit best is the optimal fitness value, fit worst is the worst fitness value, T is the maximum number of iterations, η is the adjustment parameter of the exponential change rate, I is a 1×D vector with all elements equal to 1, g is a 1×D weight vector that obeys a normal distribution, and S is a constant; By dynamically adjusting the weight vector g, the global search is balanced to enable the dung beetle optimization algorithm to escape from the local optimum. S42. Integrate the differential evolution algorithm into the dung beetle optimization algorithm, calculate the difference between the dung beetles and perform mutation operation with the global optimal dung beetle to generate a new dung beetle. The position of the new dung beetle is: Where x new t is the newly generated dung beetle, λ is the scaling factor, x r1 and x r2 is a randomly selected dung beetle location.

2. The auto-disturbance rejection control method for an automobile durability test platform based on the dung beetle optimization algorithm according to claim 1 is characterized in that: Step S1 includes the following sub-steps: S11. Construct a permanent magnet synchronous system model, ignoring minor factors such as motor damping, eddy current hysteresis energy loss, etc. In the dq coordinate system, the decoupling state equation of the permanent magnet synchronous servo system is: Where i q is the q-axis current, i d is the d-axis current, u q is the q-axis voltage, u d is the d-axis voltage, L d is the equivalent d-axis inductance, L q is the equivalent q-axis inductance, R is the equivalent resistance of the servo motor winding, P n is the number of magnetic pole pairs, J is the moment of inertia, is the equivalent flux linkage of the rotor magnetic field; S12, make i d =0 vector control, we get: Where, d-axis voltage u d is the input quantity of the permanent magnet synchronous servo motor, the rotor angular velocity w r is the output of the permanent magnet synchronous servo motor; S13. Obtain the transfer function of each servo drive control system through Laplace transform: Where, is the torque coefficient, is the position transfer function of the ball screw.

3. The auto-disturbance rejection control method for an automobile durability test platform based on the dung beetle optimization algorithm according to claim 1, characterized in that: In step S3, the position of the dung beetle when rolling the dung ball without any obstacles is updated as follows: When a dung beetle rolls a dung ball in the presence of an obstacle, its position is updated to: Where t is the number of iterations, is the position of the i-th dung beetle in the population at the t-th iteration, k∈(0,0.2] is the deflection coefficient, b is a constant between (0,1), φ is a natural number assigned -1 or 1, 1 means no deviation, -1 means deviation from the direction, The worst position in the population.

4. The auto-disturbance rejection control method for an automobile durability test platform based on the dung beetle optimization algorithm according to claim 1, characterized in that: The position of the dung beetle embryo ball in step S3 is updated as follows: Where B i t+1 is the position of the i-th chick at the t-th update, b1 and b2 are 1×D independent random vectors, Ub* and Lb* are the upper and lower bounds of the chick position, respectively, specifically:

5. The auto-disturbance rejection control method for an automobile durability test platform based on the dung beetle optimization algorithm according to claim 1, characterized in that: The foraging position of the small dung beetle in step S3 is updated to: Where C1 is a random number that obeys the normal distribution, that is, C1~N(0,1), C1 is a 1×D random vector between (0,1), Lb t and Ub t The upper and lower bounds of the dung beetle's foraging area are:

6. The auto-disturbance rejection control method for an automobile durability test platform based on the dung beetle optimization algorithm according to claim 1, characterized in that: The input end of the drive control system receives the linear motion signal and the actual linear motion signal, and converts the signal into the expected servo motor speed signal w* and the actual servo motor speed signal w according to the transmission ratio, and outputs the voltage control signal u through the improved active disturbance rejection controller. q As for the permanent magnet synchronous servo motor, the rotational motion of the permanent magnet synchronous servo motor is converted into linear motion through a ball screw, and the self-disturbance rejection control of the test platform is realized by improving the dung beetle optimization algorithm.

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