Automobile durability test platform active disturbance rejection control method based on dung beetle optimization algorithm

Abstract

The invention belongs to the technical field of automobile test platform control, and provides an automobile durability test platform active-disturbance-rejection control method based on a dung beetle optimization algorithm, and the method comprises the steps: S1, building a drive control system model of an automobile durability test platform; s2, designing an active disturbance rejection controller based on the control system model; s3, adopting a dung beetle optimization algorithm to perform self-tuning on to-be-tuned parameters in the active-disturbance-rejection controller; and S4, integrating the Kent chaos theory and the differential evolution algorithm into the dung beetle optimization algorithm, and dynamically adjusting iteration parameters. According to the method, disturbance inside and outside the system is estimated and compensated through a tracking differentiator, a nonlinear state error feedback control law and an extended state observer in the active disturbance rejection controller, the response speed and precision of the control system are improved, and Kent chaotic mapping and a differential evolution algorithm are fused into a dung beetle optimization algorithm; and iteration parameters of the dung beetle optimization algorithm are subjected to nonlinear optimization, so that the control efficiency and robustness of the system in dynamic response are improved.

Description

Technical Field

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

[0002] In order to ensure the stability and reliability of automobile performance, durability testing is essential. The durability test platform is mainly used to simulate various working conditions of the car during actual driving to evaluate its performance after long-term use, including the stability of the power system, the wear of each component, and the coordination of the overall system. As the core indicator for evaluating the performance of the automobile durability test platform, the road spectrum reproduction accuracy largely determines the quality of the automobile fatigue durability performance. In recent years, servo control technology has been increasingly widely 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 accurately controlled, so that it can simulate various dynamic conditions in the actual driving process, such as acceleration, braking, climbing, etc.

[0003] At present, the automotive durability test platform mainly relies on the traditional PID control strategy, which is reliable in most cases. However, when faced with complex dynamic test environments, it is often unable to cope with the inability to handle noise and dynamic disturbances. The ADRC can estimate and compensate for internal and external disturbances of the system to enhance the performance of the system due to its high noise immunity and robustness. It is widely used in servo control scenarios. The complexity of parameter adjustment of the ADRC makes it difficult to effectively achieve the optimal control performance, resulting in low control accuracy and slow response speed of the existing durability test platform. Therefore, it is necessary to propose an ADRC control method for the automotive durability test platform based on the dung beetle optimization algorithm. Summary of the invention

[0004] In view of 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 tracking differentiator, nonlinear state error feedback control law and extended state observer in the auto-disturbance rejection controller are used to estimate and compensate for disturbances inside and outside the system to improve the response speed and control accuracy of the control system. The Kent chaotic mapping and differential evolution algorithm are integrated into the dung beetle optimization algorithm, and the iterative parameters of the dung beetle optimization algorithm are nonlinearly improved to improve the setting efficiency and accuracy of the auto-disturbance rejection controller parameters, thereby improving the control efficiency and robustness of the system in 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 the driving control system model of the automobile 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, using the improved dung beetle optimization algorithm to self-tune multiple parameters to be adjusted in the ADRC, using the time-weighted absolute error integral of the ball screw to construct the fitness function of the ADRC, 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 coefficient β in the nonlinear state error feedback control law 1 , β 2 , associate the parameters to be adjusted with the positions of dung beetles, initialize the population and set the fitness function;

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

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

[0011]

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

[0013] x i =Lb+(Ub-Lb)×y i

[0014] In the formula, 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] In the formula, fitbest 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;

[0020] By dynamically adjusting the weight vector g, the global search is balanced to make the dung beetle optimization algorithm jump out of the local optimum;

[0021] S42, integrating the differential evolution algorithm into the dung beetle optimization algorithm, generating a new dung beetle by calculating the difference between the dung beetle and the global optimal dung beetle for mutation operation, and the position of the new dung beetle is:

[0022]

[0023] In the formula, 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] In the formula, 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] In the formula, is the torque coefficient, is the position transfer function of the ball screw.

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

[0035]

[0036] Where v is the desired linear velocity of the ball screw, q 1 is the tracking value of the expected linear velocity v, q 2 is the differential of the expected linear velocity v, fhan() is the fastest comprehensive function, and its expression is:

[0037]

[0038] In the formula, h is the sampling step, r is the speed factor, q 1 ,q 2 are the state variables of the controlled system, d, s a 、s y is the derived quantity 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] In the formula, β 01 , β 02 , β 03 is the control parameter, whose value affects the observation performance of the extended state observer, y is the actual linear drive speed of the ball screw, z 1 is the estimated value of the actual linear drive speed of the ball screw, z 2 is the estimated value of the actual linear drive velocity differential, z 3 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] In the formula, e 1 and e 2is the difference between the transition value and the observed value, u 0 is the initial control quantity, z 1 is the observed displacement, z 2 is the observation speed, β 1 and β 2 is the system gain, α 1 The value range is [0,1], α 2 >1, 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, the position of the dung beetle when rolling the dung ball without any obstacle 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] In the formula, 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] In the formula, B i t+1 is the position of the i-th chick at the t-th update, b 1 , b 2 is an independent random vector of 1×D, Ub* and Lb* are the upper and lower bounds of the position of the chicks, respectively, as follows:

[0055]

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

[0057]

[0058] In the formula, C 1 is a random number that follows a normal distribution, that is, C1 ~N(0,1),C 1 is a 1×D random vector between (0, 1), Lb t and Ub t The upper and lower bounds of the foraging area of ​​the dung beetle 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 anti-disturbance control 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.

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

[0062] The present invention is based on the Dung Mantis Optimization Algorithm for the automotive durability test platform. By introducing the Dung Mantis Optimization Algorithm, the Dung Mantis Optimization Algorithm is used to estimate and compensate for the disturbances inside and outside the system using the tracking differentiator, the nonlinear state error feedback control law and the extended state observer to improve the response speed and control accuracy of the control system. By integrating the Kent chaos map and the differential evolution algorithm into the Dung Mantis Optimization Algorithm, and improving the nonlinearity of the iteration parameters of the Dung Mantis Optimization Algorithm, the tuning efficiency and accuracy of the Dung Mantis Optimization Algorithm parameters are improved, and the control efficiency and robustness of the system in the dynamic response process are effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It 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 It is a schematic diagram of transfer functions of various drive control systems of the automobile durability test platform of the present invention;

[0065] Figure 3 It is a schematic diagram of a nonlinear auto-disturbance rejection controller for improving the dung beetle optimization algorithm in the auto-disturbance rejection control method of the automobile durability test platform of the present invention;

[0066] Figure 4 It is a tracking curve diagram of the expected displacement of each driving control system model of the automobile durability test platform of the present invention;

[0067] Figure 5 It 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] In order to fully describe the technical content, structural features, objectives and effects of the present invention, the following will be described in detail with reference to the accompanying drawings.

[0069] The present invention provides an anti-disturbance control method for an automobile durability test platform based on a dung beetle optimization algorithm. 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] In the formula, 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.

[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] In the formula, 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, a discrete form of the tracking differentiator is constructed, specifically:

[0082]

[0083] Where v is the desired linear velocity of the ball screw, q 1 is the tracking value of the expected linear velocity v, q 2 is the differential of the expected linear velocity v, fhan() is the fastest comprehensive function, and its expression is:

[0084]

[0085] In the formula, h is the sampling step, r is the speed factor, q 1 ,q 2 are the state variables of the controlled system, d, s a 、s y is the derived quantity 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] In the formula, β 01 , β 02 , β 03 is the control parameter, whose value affects the observation performance of the extended state observer, y is the actual linear drive speed of the ball screw, z 1 is the estimated value of the actual linear drive speed of the ball screw, z 2 is the estimated value of the actual linear drive velocity differential, z 3 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] In the formula, e 1 and e 2 is the difference between the transition value and the observed value, u 0 is the initial control quantity, z 1 is the observed displacement, z 2 is the observation speed, β 1 and β 2 is the system gain, α 1 The value range is [0,1], α2 >1, 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, using the improved dung beetle optimization algorithm to self-tune multiple parameters to be adjusted in the ADRC, using the time-weighted absolute error integral of the ball screw to construct the fitness function of the ADRC, 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 coefficient β in the nonlinear state error feedback control law 1 , β 2 , associate the parameters to be adjusted with the positions of dung beetles, initialize the population and set the fitness function.

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

[0096]

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

[0098]

[0099] In the formula, 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] In the formula, B i t+1 is the position of the i-th chick at the t-th update, b 1 , b 2 is an independent random vector of 1×D, Ub* and Lb* are the upper and lower bounds of the position of the chicks, respectively, as follows:

[0103]

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

[0105]

[0106] In the formula, C 1 is a random number that follows a normal distribution, that is, C 1 ~N(0,1),C 1 is a 1×D random vector between (0, 1), Lb t and Ub t The upper and lower bounds of the foraging area of ​​the dung beetle are:

[0107]

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

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

[0110]

[0111] Bringing Kent chaotic mapping 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] In the formula, fit best is the optimal fitness value, fit worstis 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 whose elements are all 1, g is a 1×D weight vector that obeys a 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 the local optimum and the ability to search globally are balanced.

[0120] S42, integrating the differential evolution algorithm into the dung beetle optimization algorithm, generating a new dung beetle by calculating the difference between the dung beetle and the global optimal dung beetle for mutation operation, and the position of the new dung beetle is:

[0121]

[0122] In the formula, 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 drive control system of the automobile durability test platform 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 anti-disturbance control 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.

[0124] The following is a further description of the auto-disturbance rejection control method of 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 automobile 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 the figure, 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 and the system gain coefficient β in the nonlinear state error feedback control law 1 , β2 As the parameter to be adjusted, the specific value after optimization is: the gain coefficient β in the extended state observer 01 =58, β 02 =1000, β 03 =145, the system gain coefficient β in the nonlinear state error feedback control law 1 =1000, β 2 =145, the remaining parameters are a 1 =0.5, a 2 =1.25,δ=0.05.

[0129] like Figure 4 and Figure 5 As shown, the nonlinear anti-disturbance 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 small tracking error and good dynamic performance.

[0130] The present invention is based on the dung beetle optimization algorithm of the automobile durability test platform auto-disturbance rejection control method. Aiming at the problem that the parameters of the auto-disturbance rejection controller are difficult to adjust, the dung beetle optimization algorithm is introduced to find the optimal parameters of the auto-disturbance rejection controller. Aiming at the shortcomings that the dung beetle optimization algorithm is sensitive to parameters and has pseudo-randomness, the Kent chaotic mapping is introduced to initialize the population of the dung beetle optimization algorithm. In order to enhance the local search capability of the dung beetle optimization algorithm in the early iteration and the global search capability in the late iteration, the iteration parameters of the dung beetle optimization algorithm are nonlinearly dynamically adjusted, and combined with the differential evolution algorithm, the ability of the dung beetle optimization algorithm to jump out of the local optimum is enhanced, and the parameter adjustment efficiency is improved. The method of the present invention improves the control accuracy of the servo control branch of the automobile durability test platform, thereby improving the terminal accuracy of the automobile durability test platform and improving the road spectrum recurrence rate.

[0131] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design 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 all fall within the protection scope 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 the driving control system model of the automobile 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; 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, the parameters to be tuned are associated with the positions of dung beetles, the population is initialized and the fitness function is set; S4. Integrate Kent chaos theory and differential evolution algorithm into the dung beetle optimization algorithm, dynamically adjust the iteration parameters of the stealing dung beetle in the dung beetle optimization algorithm in a nonlinear way, and dynamically update the optimized parameters to the extended state observer and nonlinear state error feedback control law of the self-disturbance rejection controller to realize the self-disturbance rejection control of the vehicle test platform motion. The sub-steps include: S41. The population is initialized using the dung beetle optimization algorithm based on Kent chaos mapping. The expression of Kent chaos mapping is: Bringing Kent chaotic mapping into the population initialization of the dung beetle optimization algorithm, its expression is: x i =Lb+(Ub-Lb)×y i In the formula, 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 stealing dung beetle's position update: The location of the stealing dung beetle has been updated to: In the formula, 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 make the dung beetle optimization algorithm jump out of the local optimum; S42, integrating the differential evolution algorithm into the dung beetle optimization algorithm, generating a new dung beetle by calculating the dung beetle difference and performing a mutation operation with the global optimal dung beetle, and the position of the new dung beetle is: In the formula, 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 method for controlling the vehicle 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: In the formula, 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: In the formula, is the torque coefficient, is the position transfer function of the ball screw.

3. The method for controlling the vehicle durability test platform based on the dung beetle optimization algorithm according to claim 1 is characterized in that: In step S2, a discrete form of the tracking differentiator is constructed, 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 comprehensive function, which is expressed as follows: In the formula, 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 derived quantity of the function fhan().

4. The method for controlling the vehicle durability test platform based on the dung beetle optimization algorithm according to claim 1 is characterized in that: 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: In the formula, β 01 , β 02 , β 03 is a control parameter whose 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 value, 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.

5. The method for controlling the vehicle durability test platform based on the dung beetle optimization algorithm according to claim 1 is characterized in that: In step S3, the position of the dung beetle when rolling the dung ball without obstacles is updated to: When a dung beetle rolls a dung ball in the presence of an obstacle, its position is updated to: In the formula, 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.

6. The method for controlling the vehicle durability test platform based on the dung beetle optimization algorithm according to claim 1 is characterized in that: The position of the dung beetle embryo ball in step S3 is updated as follows: In the formula, 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, as follows:

7. The method for controlling the vehicle durability test platform based on the dung beetle optimization algorithm according to claim 1 is 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 foraging area of ​​the dung beetle are:

8. The method for controlling the vehicle durability test platform based on the dung beetle optimization algorithm according to claim 1 is 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 anti-disturbance control 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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