Active disturbance rejection control method suitable for distributed driving vehicle suspension system

Abstract

The invention belongs to the technical field of vehicle suspension vibration isolation, and particularly relates to a distributed driving vehicle suspension system and an active-disturbance-rejection control method, and the method comprises the following steps: 1, constructing a distributed driving vehicle suspension structure model based on active-disturbance-rejection control; 2, analyzing and expressing the active disturbance rejection control method; step 3, solving an internal parameter and a target function of the active-disturbance-rejection control strategy with the optimal performance by adopting an optimization algorithm; and 4, performing dynamic performance simulation analysis. The method has the beneficial effects that the dynamic characteristics of the complex system can be described more accurately; and the riding comfort of the vehicle is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle suspension vibration isolation, and particularly relates to a distributed drive vehicle suspension system and an active disturbance rejection control method. Background Art

[0002] Due to its good vibration isolation performance and easy-to-implement control, semi-active suspension achieves the best compromise among manufacturing cost, equipment complexity, energy consumption, and suspension performance, and has been widely studied and applied in recent years. However, since the realization of semi-active suspension is based on a fixed suspension structure, it is difficult for semi-active suspension to ensure optimal performance under different road conditions and different vehicle operating conditions. The technical bottleneck of semi-active suspension systems lies in the design of control strategies, and the quality of control strategies directly affects the dynamic characteristics of vehicles. As the most widely used industrial control strategy at present, the PID control strategy still has problems such as easy overshoot of system behavior, deterioration of dynamic characteristics when the system is undisturbed, and limited dynamic quality margin, which restricts the further improvement of suspension performance.

[0003] In view of the above situation, it is necessary to improve the existing semi-active suspension control method so that it can meet the current needs of semi-active suspension control. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] The technical problem to be solved by the present invention is: in order to overcome the shortcomings of the above control method, how to provide a distributed drive vehicle suspension system and an active disturbance rejection control method, which can more accurately describe the dynamic characteristics of complex systems and further improve the ride comfort of vehicles.

[0006] (II) Technical Solutions

[0007] To solve the above technical problems, the present invention provides an active disturbance rejection control method applicable to a distributed drive vehicle suspension system, and the method includes the following steps:

[0008] Step 1: Construct a distributed drive vehicle suspension structure model based on active disturbance rejection control;

[0009] Step 2: Analytically express the active disturbance rejection control method;

[0010] Step 3: Use an optimization algorithm to solve the internal parameters and objective function of the active disturbance rejection control strategy with optimal performance;

[0011] Step 4: Dynamic performance simulation analysis.

[0012] Among them, in the above Step 1, an electromechanical inertance container used as a semi-active actuator in parallel is used to achieve the purpose of active disturbance rejection control.

[0013] Among them, in the first step, the dynamic equation of the quarter-distributed drive vehicle suspension system for constructing the suspension structure model is as follows:

[0014]

[0015] Among them, m s is the unsprung mass, k is the stiffness of the supporting spring, z s is the vertical displacement of the unsprung mass, z u is the vertical displacement of the sprung mass, b is the inertance coefficient, c is the damping coefficient, F d is the output force of the rotating motor in the electromechanical inertance container;

[0016] m u is the sprung mass, k t is the equivalent spring stiffness of the tire, z r is the vertical input displacement of the road surface roughness;

[0017] is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass.

[0018] Among them, in the distributed drive vehicle suspension system, the output force F d of the rotating motor in the electromechanical inertance container is used as the semi-active control force and is determined by the process of active disturbance rejection control.

[0019] Among them, in the second step, the process of active disturbance rejection control involves a tracking differentiator, a nonlinear error feedback controller, and an extended state observer, which are used to control the output force F d of the electromechanical inertance container, and list its analytical expression.

[0020] Among them, the analytical expressions of the tracking differentiator, the nonlinear error feedback controller, and the extended state observer involved in the process of active disturbance rejection control are respectively:

[0021] The mathematical expression of the tracking differentiator is:

[0022]

[0023] Among them, fhan is the fastest tracking function, v1 is the tracking signal, v2 is the differential signal, r0 is the speed factor, and h is the filtering factor;

[0024] The mathematical expression of the extended state observer is:

[0025]

[0026] Among them, e Eis the error between the measured displacement value of the unsprung mass and the displacement observation value, z1 is the actual measured displacement value of the unsprung mass, and y is the displacement observation value of the unsprung mass;

[0027] fal is a non - linear function;

[0028] The expression of the non - linear function fal is:

[0029]

[0030] δ E is the boundary for distinguishing the magnitude of e E and is a linear factor;

[0031] z2 is the differential of z1;

[0032] β E1 、β E2 and β E3 are all observation factors and are adjustable parameters; z3 is the disturbance estimation value, b0 is the compensation factor, and u is the output force of the rotary motor;

[0033] The mathematical expression of the non - linear state error feedback controller is:

[0034]

[0035] e N1 、e N2 are the state errors of the system, and u0 is the error feedback control quantity;

[0036] β N1 and β N2 are all gain factors and are adjustable parameters;

[0037] δ N is the boundary for distinguishing the magnitude of e N and is a linear factor;

[0038] α N1 and α N2 are non - linear factors;

[0039] Among them, during the optimization process, the ranges of relevant variables are set as follows:

[0040]

[0041] Among them, in step three, taking the ride comfort in vehicle driving performance as the evaluation index, by studying the root - mean - square value of the body acceleration under random road input conditions and using the particle swarm algorithm, the internal parameters of the active disturbance rejection control strategy with optimal performance are obtained.

[0042] Among them, by studying the dynamic performance indexes including the root mean square value of the vehicle body acceleration under the condition of random road input, the objective function of the optimization algorithm in the third step is as follows:

[0043]

[0044] Among them, f is the objective function of the optimization algorithm, J is the root mean square value of the vehicle body acceleration of the active disturbance rejection control suspension, and J pas is the root mean square value of the vehicle body acceleration of the traditional passive suspension;

[0045] The mathematical expression of J is as follows:

[0046]

[0047] In the formula, BA is the vehicle body acceleration, N is the number of samples, and i is the sample serial number.

[0048] Among them, in the third step, the particle swarm optimization algorithm is used to solve the optimization parameters, and the relationship between the particle position and velocity is as follows:

[0049]

[0050] Among them, is the velocity vector of particle i in the d-th dimension at the (j + 1)-th iteration; β is the inertia weight; is the velocity vector of particle i in the d-th dimension at the j-th iteration; c1 is the individual learning factor; c2 is the swarm learning factor; r1 and r2 are random numbers; is the optimal position of the particle in the d-th dimension at the j-th iteration; is the optimal position of the swarm in the d-th dimension at the j-th iteration; is the displacement vector of particle i in the d-th dimension at the (j + 1)-th iteration; is the displacement vector of particle i in the d-th dimension at the j-th iteration;

[0051] During the optimization process, the constraint range of the performance index is as follows:

[0052] J ≤ J pas

[0053] Among them, if the performance index constraint is exceeded, the objective function will be penalized; the penalty rule is to add a large number to the objective function, and the large number, that is, the penalty value, is set to 100.

[0054] Among them, the specific implementation method of the fourth step is: determine the suspension model and suspension parameters of the distributed drive vehicle, and drive at a speed of 60 km / h over a road surface unevenness coefficient of 256×10 -6 m 3 ·cycle -1The road surface has a simulation duration of 10 s and a sampling interval of 0.02 s. A random road surface model determined by Gaussian white noise with a mean of zero is selected. The working performance indexes of the vehicle body acceleration suspension under the condition of random road surface input for PID control and active disturbance rejection control are calculated and compared with the corresponding indexes of the passive suspension.

[0055] (III) Beneficial effects

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] (1) The present invention combines active disturbance rejection control with an electromechanical inertial capacitor, proposes an application method of an electromechanical inertial capacitor based on active disturbance rejection control in a distributed drive vehicle suspension system, and proposes a control method for an electromechanical inertial capacitor based on active disturbance rejection control, effectively improving the performance of the electromechanical inertial capacitor.

[0058] (2) The distributed drive vehicle suspension system and the active disturbance rejection control method proposed by the present invention utilize the characteristics of fast local convergence speed and high solution efficiency of the particle swarm algorithm, and can obtain the internal parameters of the optimal active disturbance rejection control strategy. The simulation shows that, comparing the suspension performance of the semi-active suspension and the passive suspension of PID control and active disturbance rejection control, the active disturbance rejection control can estimate and compensate the internal and external disturbances of the system model in real time, and at the same time eliminate the hysteresis effect generated by the integral link, effectively improving the anti-interference performance of the control system. The distributed drive vehicle suspension based on active disturbance rejection control can further suppress the vehicle body acceleration and improve the vehicle ride comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flowchart of the distributed drive vehicle suspension system and the active disturbance rejection control method of the present invention.

[0060] Figure 2 is a schematic diagram of the dynamic model of a quarter distributed drive vehicle suspension. DETAILED DESCRIPTION OF THE INVENTION

[0061] In order to make the objectives, contents, and advantages of the present invention clearer, the following further describes in detail the specific embodiments of the present invention with reference to the drawings and embodiments.

[0062] To solve the above technical problems, the present invention provides an active disturbance rejection control method applicable to a distributed drive vehicle suspension system. The method includes the following steps:

[0063] Step 1: Construct a distributed drive vehicle suspension structure model based on active disturbance rejection control;

[0064] Step 2: Analytically express the active disturbance rejection control method;

[0065] Step 3: Use an optimization algorithm to solve the internal parameters of the optimal active disturbance rejection control strategy and the objective function;

[0066] Step 4: Dynamic performance simulation analysis.

[0067] Among them, in the said Step 1, an electromechanical inertial capacitor used as a semi-active actuator in parallel is utilized to achieve the purpose of active disturbance rejection control.

[0068] Among them, in the said Step 1, the dynamic equation of the quarter-distributed drive vehicle suspension system for constructing the suspension structure model is:

[0069]

[0070] Among them, m s is the sprung mass, k is the stiffness of the supporting spring, z s is the vertical displacement of the sprung mass, z u is the vertical displacement of the unsprung mass, b is the inertance coefficient, c is the damping coefficient, F d is the output force of the rotating motor in the electromechanical inertial capacitor;

[0071] m u is the unsprung mass, k t is the equivalent spring stiffness of the tire, z r is the vertical input displacement of the road surface roughness;

[0072] is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass.

[0073] Among them, in the distributed drive vehicle suspension system, the output force F d of the rotating motor in the electromechanical inertial capacitor is used as the semi-active control force and is determined by the process of active disturbance rejection control.

[0074] Among them, in the said Step 2, the process of active disturbance rejection control involves a tracking differentiator, a nonlinear error feedback controller, and an extended state observer, which are used to control the output force F d of the electromechanical inertial capacitor, and list its analytical expressions.

[0075] Among them, the analytical expressions of the tracking differentiator, the nonlinear error feedback controller, and the extended state observer involved in the process of active disturbance rejection control are respectively:

[0076] The mathematical expression of the tracking differentiator is:

[0077]

[0078] Among them, fhan is the fastest tracking function, v1 is the tracking signal, v2 is the differential signal, r0 is the speed factor, and h is the filtering factor;

[0079] The mathematical expression of the extended state observer is:

[0080]

[0081] Among them, e E is the error between the measured displacement value of the unsprung mass and the displacement observation value, z1 is the actual measured displacement value of the unsprung mass, and y is the displacement observation value of the unsprung mass;

[0082] fal is a nonlinear function;

[0083] The expression of the nonlinear function fal is:

[0084]

[0085] δ E is the boundary for distinguishing the magnitude of e E and is the linear factor;

[0086] z2 is the differential of z1;

[0087] β E1 、β E2 and β E3 are all observation factors and are adjustable parameters; z3 is the disturbance estimation value, b0 is the compensation factor, and u is the output force of the rotating motor;

[0088] The mathematical expression of the nonlinear state error feedback controller is:

[0089]

[0090] e N1 、e N2 are the state errors of the system, and u0 is the error feedback control quantity;

[0091] β N1 and β N2 are both gain factors and are adjustable parameters;

[0092] δ N is the boundary for distinguishing the magnitude of e N and is the linear factor;

[0093] α N1 and α N2 are nonlinear factors;

[0094] Among them, during the optimization process, the ranges of relevant variables are set as follows:

[0095]

[0096] Among them, in the third step, taking the ride comfort in vehicle driving performance as the evaluation index, by studying the root mean square value of the vehicle body acceleration under random road input conditions, and using the particle swarm algorithm, the internal parameters of the active disturbance rejection control strategy with optimal performance are obtained.

[0097] Among them, by studying the dynamic performance index including the root mean square value of the vehicle body acceleration under random road input conditions, the objective function of the optimization algorithm in the third step is:

[0098]

[0099] Among them, f is the objective function of the optimization algorithm, J is the root mean square value of the active disturbance rejection control suspension vehicle body acceleration, and J pas is the root mean square value of the traditional passive suspension vehicle body acceleration;

[0100] The mathematical expression of J is as follows:

[0101]

[0102] In the formula, BA is the vehicle body acceleration, N is the number of samples, and i is the sample serial number.

[0103] Among them, in the third step, the particle swarm optimization algorithm is used to solve the optimization parameters, and the relationship between the particle position and velocity is as follows:

[0104]

[0105] Among them, is the velocity vector of particle i in the d-th dimension at the (j + 1)-th iteration; β is the inertia weight; is the velocity vector of particle i in the d-th dimension at the j-th iteration; c1 is the individual learning factor; c2 is the swarm learning factor; r1 and r2 are random numbers; is the optimal position of the particle in the d-th dimension at the j-th iteration; is the optimal position of the swarm in the d-th dimension at the j-th iteration; is the displacement vector of particle i in the d-th dimension at the (j + 1)-th iteration; is the displacement vector of particle i in the d-th dimension at the j-th iteration;

[0106] During the optimization process, the constraint range of the performance index is as follows:

[0107] J ≤ J pas

[0108] Among them, if the performance index constraint is exceeded, the objective function will be punished; the punishment rule is to add a large number to the objective function, and this large number, that is, the punishment value, is set to 100.

[0109] Among them, the specific implementation method of the fourth step is as follows: Determine the suspension model and suspension parameters of the distributed drive vehicle, drive at a speed of 60 km / h on a road surface with a road surface unevenness coefficient of 256×10 -6 m 3 ·cycle -1 , simulate for 10 s with a sampling interval of 0.02 s, select a random road surface model determined by Gaussian white noise with a mean of zero, calculate the body acceleration suspension working performance indexes of PID control and active disturbance rejection control under the condition of random road surface input, and compare them with the corresponding indexes of the passive suspension.

[0110] Example 1

[0111] As Figure 1 shown, this embodiment provides a distributed drive vehicle suspension system and an active disturbance rejection control method, which mainly include the following steps:

[0112] Step 1: Construct a distributed drive vehicle suspension structure model based on active disturbance rejection control;

[0113] Step 2: Analytically express the active disturbance rejection control method;

[0114] Step 3: Use an optimization algorithm to solve the internal parameters and objective function of the active disturbance rejection control strategy with the optimal performance;

[0115] Step 4: Dynamic performance simulation analysis;

[0116] Regarding the distributed drive vehicle suspension based on active disturbance rejection control considered in the first step, in this paper, it is equivalent to the model as Figure 2 shown, and an electromechanical inertia container used as a semi-active actuator in parallel is used to achieve the purpose of active disturbance rejection control.

[0117] Taking the distributed drive vehicle suspension with active disturbance rejection control in the first step as an example Figure 2 shown, the dynamic equation of the quarter distributed drive vehicle suspension system for constructing the suspension structure model is:

[0118]

[0119] In the formula, where m s is the unsprung mass, k is the stiffness of the support spring, z s is the vertical displacement of the unsprung mass, z u is the vertical displacement of the sprung mass, b is the inertance coefficient, c is the damping coefficient, F d is the output force of the rotating motor in the electromechanical inertia container;

[0120] m u is the sprung mass, k t is the equivalent spring stiffness of the tire, zr is the vertical input displacement of road surface roughness;

[0121] is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass, is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass.

[0122] Based on a mature vehicle model, the parameters of the quarter distributed drive vehicle suspension system are shown in Table 1.

[0123] Table 1 Quarter distributed drive vehicle suspension parameters

[0124]

[0125] In the distributed drive vehicle suspension system, the output force F of the rotary motor in the electromechanical inertia container d acts as the semi-active control force and is determined by the process of active disturbance rejection control.

[0126] The process of active disturbance rejection control involves a tracking differentiator, a nonlinear error feedback controller, and an extended state observer, which are used to control the output force F of the rotary motor in the electromechanical inertia container d , and list its analytical expression.

[0127] The mathematical expression of the tracking differentiator is:

[0128]

[0129] where: v0 is the reference signal, v1 is the tracking signal, v2 is the differential signal, fhan is the fastest tracking function, r0 is the speed factor, and h is the filtering factor.

[0130] Since the main purpose of the controller is to improve the suspension performance, and the body acceleration is its main evaluation index. At the same time, when the body acceleration is always maintained near 0, the ride comfort of the HMDV (Hub Motor drive vehicle) is the best. Therefore, the reference signal is set as the body acceleration, then v0 = 0, and we can get:

[0131]

[0132] The specific expression of fhan:

[0133]

[0134] The mathematical expression of the extended state observer is:

[0135]

[0136] where, e E is the error between the measured displacement value of the unsprung mass and the displacement observation value, z1 is the actual measured displacement value of the unsprung mass, and y is the displacement observation value of the unsprung mass;

[0137] fe and fe1 are the estimator quantities of the observer system, and fal() is a nonlinear function;

[0138] δ E is the boundary for distinguishing the magnitude of e E and is a linear factor;

[0139] h() is the sampling period, and z2 is the derivative of z1;

[0140] β E1 , β E2 and β E3 are all observation factors and are adjustable parameters; z3 is the disturbance estimation value, b0 is the compensation factor, and u is the output force of the rotating motor.

[0141] The expression of the nonlinear function fal is:

[0142]

[0143] where, α E is the nonlinear factor, following the idea of large error with small gain and small error with large gain, and its range is usually within 0 to 1.

[0144] is the nonlinear disturbance compensation.

[0145] The mathematical expression of the nonlinear state error feedback controller:

[0146]

[0147] where, e N1 , e N2 are the state errors of the system, and u0 is the error feedback control quantity;

[0148] β N1 and β N2 are both gain factors and are adjustable parameters;

[0149] δ N is the boundary for distinguishing the magnitude of e N and is a linear factor;

[0150] α N1 and α N2 are nonlinear factors;

[0151] The mathematical expression for its disturbance compensation part is:

[0152]

[0153] For further supplement to this technical solution, in step three, taking the ride comfort in vehicle driving performance as the evaluation index, by studying the root mean square value of vehicle body acceleration under random road input conditions and using the particle swarm algorithm, the internal parameters of the active disturbance rejection control strategy with optimal performance can be obtained.

[0154] The parameters that need to be adjusted in the active disturbance rejection control are as follows: in the tracking differentiator, the speed factor r0 and the filtering factor h need to be adjusted; in the extended state observer, the compensation factor b0, the linear factor δ E and the observation factor β E1 、β E2 and β E3 need to be adjusted; in the nonlinear state error feedback controller, the linear factor δ N and the gain factor β N1 and β N2 need to be adjusted. The integration step size h0 of this control is 0.01. At the same time, among the above parameters that need to be adjusted, according to control experience, the filtering factor can be set to 4 times the integration step size, the speed factor is 0.05, the compensation factor b0 is in the range of [0.01 - 10], the linear factor δ E is 0.02, δ N1 is 0.25, and δ N2 is 0.5.

[0155] For further supplement to this technical solution, the dynamic performance indexes including the root mean square value of vehicle body acceleration under random road input conditions are studied. The objective function of the optimization algorithm in step three is as follows:

[0156]

[0157] where f is the objective function of the optimization algorithm, J is the root mean square value of the vehicle body acceleration of the active disturbance rejection control suspension, and J pas is the root mean square value of the vehicle body acceleration of the traditional passive suspension.

[0158] The mathematical expression of J is as follows:

[0159]

[0160] In the formula, BA is the vehicle body acceleration, N is the number of samples, and i is the sample serial number.

[0161] The optimization variables are selected as follows: the compensation factor b0, the observation factor β E1 、β E2 and β E3 and the gain factor β N1 and β N2 ; during the optimization process, the ranges of the variables are set as follows:

[0162]

[0163] For further supplement to this technical solution, the particle swarm optimization algorithm is used to solve the optimization parameters. The relationship between the particle position and velocity is as follows:

[0164]

[0165] Among them, is the velocity vector of particle i in the d-th dimension at the (j + 1)-th iteration; β is the inertia weight; is the velocity vector of particle i in the d-th dimension at the j-th iteration; c1 is the individual learning factor; c2 is the swarm learning factor; r1 and r2 are random numbers; is the optimal position of the particle in the d-th dimension at the j-th iteration; is the optimal position of the swarm in the d-th dimension at the j-th iteration; is the displacement vector of particle i in the d-th dimension at the (j + 1)-th iteration; is the displacement vector of particle i in the d-th dimension at the j-th iteration;

[0166] During the optimization process, the constraint range of the performance index is as follows:

[0167] J ≤ J pas

[0168] Among them, if the performance index constraint is exceeded, the objective function will be penalized; the penalty rule is to add a large number to the objective function, and this large number, that is, the penalty value, is set to 100.

[0169] After repeated optimization, the finally optimized variable values are shown in Table 2.

[0170] Table 2 Suspension parameters of the distributed drive vehicle after optimizing the model parameters

[0171]

[0172]

[0173] In the fourth step, dynamic performance simulation analysis, the specific implementation method is as follows:

[0174] According to Figure 2 the established distributed drive vehicle suspension model, using the suspension parameters in Table 1, at a vehicle speed of 60 km / h, driving over a road surface unevenness coefficient of 256×10 -6 m 3 ·cycle -1The road surface has a simulation duration of 10 s and a sampling interval of 0.02 s. A random road surface model determined by Gaussian white noise with a mean of zero is selected. The body acceleration suspension performance indicators of PID control and active disturbance rejection control under random road surface input conditions are calculated and compared with the corresponding indicators of passive suspension. The results are shown in Table 3.

[0175] Table 3 Suspension performance indicators under random road surface input

[0176]

[0177] It can be seen from the simulation of the random road surface that the electro-mechanical inertia container based on active disturbance rejection control further improves the body vibration suppression performance of the distributed drive vehicle suspension. It also proves that the active disturbance rejection control method can more efficiently cope with complex road surface environments, further improve the ride comfort of the vehicle, and has good performance.

[0178] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and deformations can be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. A self-disturbance rejection control method applicable to a suspension system of a distributed drive vehicle, characterized in that The method includes the following steps: Step 1: Construct a distributed drive vehicle suspension structure model based on active disturbance rejection control; Step 2: Analytically express the active disturbance rejection control method; Step 3: Use an optimization algorithm to solve the internal parameters and objective function of the active disturbance rejection control strategy with optimal performance; Step 4: Dynamic performance simulation analysis.

2. The active disturbance rejection control method for a suspension system applicable to a distributed drive vehicle according to claim 1, wherein In the above Step 1, an electromechanical inertial capacitor used as a semi-active actuator in parallel is utilized to achieve the purpose of active disturbance rejection control.

3. The active disturbance rejection control method for a suspension system applicable to a distributed drive vehicle according to claim 2, characterized in that In the above Step 1, the dynamic equation of a quarter distributed drive vehicle suspension system for constructing the suspension structure model is: Among them, m s is the unsprung mass, k is the stiffness of the supporting spring, z s is the vertical displacement of the unsprung mass, z u is the vertical displacement of the sprung mass, b is the inertance coefficient, c is the damping coefficient, F d is the output force of the rotating motor in the electromechanical inertance container; m u is the unsprung mass, k t is the equivalent spring stiffness of the tire, z r is the vertical input displacement of the road surface roughness; is the vertical velocity of the unsprung mass, is the vertical acceleration of the unsprung mass, is the vertical velocity of the sprung mass, is the vertical acceleration of the sprung mass.

4. The active disturbance rejection control method for a suspension system applicable to a distributed drive vehicle according to claim 3, characterized in that In a distributed drive vehicle suspension system, the output force F of the rotary motor in the electromechanical inertance container d As a semi-active control force, it is determined by the process influence of the active disturbance rejection control.

5. The active disturbance rejection control method for a distributed drive vehicle suspension system according to claim 1, characterized in that, In the second step, the process of active disturbance rejection control involves a tracking differentiator, a nonlinear error feedback controller, and an extended state observer, which are used to control the output force F of the electromechanical inertance container. d , and list its analytical expression.

6. The active disturbance rejection control method for a suspension system applicable to a distributed drive vehicle according to claim 5, wherein The analytical expressions of the tracking differentiator, nonlinear error feedback controller, and extended state observer involved in the process of active disturbance rejection control are respectively: The mathematical expression of the tracking differentiator is: where fhan is the fastest tracking function, v1 is the tracking signal, v2 is the differential signal, r0 is the speed factor, and h is the filtering factor; The mathematical expression of the extended state observer is: Among them, e E is the error between the measured displacement value of the unsprung mass and the displacement observation value, z1 is the actual measured displacement value of the unsprung mass, and y is the displacement observation value of the unsprung mass; fal is a nonlinear function; The expression of the nonlinear function fal is: δ E To distinguish e E The boundary of the size, which is a linear factor; z2 is the derivative of z1; β E1 , β E2 and β E3 are all observation factors and adjustable parameters; z3 is the disturbance estimate, b0 is the compensation factor, and u is the output force of the rotating motor; The mathematical expression of the nonlinear state error feedback controller is: e N1 、e N2 is the state error of the system, and u0 is the error feedback control quantity; β N1 and β N2 are both gain factors and are adjustable parameters; δ N To distinguish e N The boundary of the size, which is a linear factor; α N1 and α N2 are non-linear factors; where, in the optimization process, the ranges of relevant variables are set as follows:

7. The active disturbance rejection control method for a distributed drive vehicle suspension system according to claim 6, characterized in that In the above Step 3, taking ride comfort in vehicle driving performance as the evaluation index, by studying the root mean square value of body acceleration under random road input conditions, the internal parameters of the active disturbance rejection control strategy with optimal performance are obtained using the particle swarm algorithm.

8. The active disturbance rejection control method for a distributed drive vehicle suspension system according to claim 7, characterized in that By studying dynamic performance indexes including the root mean square value of body acceleration under random road input conditions, the objective function of the optimization algorithm in Step 3 is: where f is the objective function of the optimization algorithm, J is the root mean square value of the body acceleration of the active disturbance rejection control suspension, and J pas is the root mean square value of the body acceleration of the traditional passive suspension; The mathematical expression of J is as follows: In the formula, BA is the body acceleration, N is the number of samples, and i is the sample serial number.

9. The active disturbance rejection control method for a suspension system applicable to a distributed drive vehicle according to claim 8, characterized in that, In the above Step 3, the particle swarm optimization algorithm is used to solve the optimization parameters, and the relationship between the particle position and velocity is as follows: Among them, is the velocity vector of particle i in the d-th dimension at the (j + 1)-th iteration; β is the inertia weight; is the velocity vector of particle i in the d-th dimension at the j-th iteration; c1 is the individual learning factor; c2 is the swarm learning factor; r1 and r2 are random numbers; is the optimal position of the particle in the d-th dimension at the j-th iteration; is the optimal position of the swarm in the d-th dimension at the j-th iteration; is the displacement vector of particle i in the d-th dimension at the (j + 1)-th iteration; is the displacement vector of particle i in the d-th dimension at the j-th iteration; In the optimization process, the constraint ranges of the performance indexes are as follows: J ≤ J pas If the performance index constraints are exceeded, the objective function will be penalized; the penalty rule is to add a large number to the objective function, and the large number, i.e., the penalty value, is set to 100.

10. The active disturbance rejection control method for a suspension system applicable to a distributed drive vehicle according to claim 1, wherein The specific implementation method of Step 4 is as follows: Determine the suspension model and suspension parameters of the distributed drive vehicle, drive through a road surface with a road surface unevenness coefficient of 256×10 -6 m 3 ·cycle -1 at a vehicle speed of 60 km / h. The simulation duration is 10 s, the sampling interval is 0.02 s, a random road surface model determined by Gaussian white noise with a mean of zero is selected, and the body acceleration suspension working performance indexes of PID control and active disturbance rejection control under random road surface input conditions are calculated and compared with the corresponding indexes of the passive suspension.

Images