A robust stability control method for special vehicle under soft and rough road surface

Abstract

The application discloses a kind of soft rugged terrain under special vehicle stability robust control method, it is related to vehicle control technical field. Including: obtaining driver input, based on reference generation model, combine vehicle kinematics and dynamics principle, generate the reference input of controller;Linear form model is constructed as the basic model for control, real-time online data generated by real vehicle system operation is used, model error estimation is carried out, model based on disturbance estimation is constructed, real-time correction and compensation of vehicle dynamics model are realized;Based on the off-road vehicle dynamics model after correction and compensation, respectively through the yaw stability control based on MPC, based on optimization torque distribution, control quantity is generated and sent to vehicle, and the stability control of vehicle is realized.The application can dynamically adapt the complex working conditions of soft rugged terrain, make the vehicle dynamics model more accurate, and lay a reliable foundation for subsequent control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, more particularly, to a special vehicle stability robust control method under soft and rugged road surface. BACKGROUND

[0002] In view of the complex and changeable characteristics of off-road terrain, including rugged, sandy, muddy, steep, rocky and gully terrain conditions, the dynamic changes of the relationship between road friction coefficient and wheel ground may occur. Under soft road conditions such as sandy soil and snow, the interaction between the wheel and the ground involves complex mechanical phenomena such as sinking and sliding. Under steep or side slope conditions, the center of gravity of the vehicle may shift, and the dynamic response is significantly different from driving on flat roads. Traditional methods are difficult to construct an accurate vehicle dynamics model, so it is difficult to accurately predict the vehicle response, which makes the motion control task of off-road vehicles more challenging.

[0003] Most of the existing methods currently focus only on improving the vehicle control performance under a single working condition, and it is difficult to fully cover the needs of multiple off-road working conditions. For example, patent number CN11963661A proposes an off-road vehicle motion control method and device based on model predictive control (MPC), which partially solves the stability problem when driving at high speed. Although this method considers the physical characteristics of off-road terrain and establishes a terrain dynamics model, the modeling method based on physical laws is too idealized to accurately capture the wheel-ground relationship under strong uncertainty road conditions, which may affect the algorithm's ability to guarantee terrain constraints under rugged or gully road conditions. In addition, patent number CN119389193A provides an off-road vehicle speed control device and method for steep slopes, and the rule-based method used also has an idealistic tendency, and does not fully consider the uncertainty of road conditions, nor does it cover the problem of wheel skidding and sinking under soft or slippery roads, which may lead to a decrease in vehicle stability.

[0004] In summary, there are two major problems in achieving safe and stable vehicle control under off-road conditions. The first is how to deal with the wheel-ground relationship modeling challenge under complex and changeable road conditions, and to build a more accurate vehicle dynamics model to improve the stability control effect; the second is that there is currently a lack of full-condition control scheme that can simultaneously consider the lateral and longitudinal stability of the vehicle. Therefore, it is of great significance to develop an off-road vehicle motion control strategy suitable for all working conditions.

[0005] Therefore, a special vehicle stability robust control method under soft and rugged road surface is proposed to solve the difficulties existing in the prior art, which is a problem that technicians in the field urgently need to solve. SUMMARY

[0006] In view of the above, the present application provides a kind of soft and rugged road under special vehicle stability robust control method, to solve the technical problems existing in prior art.

[0007] In order to achieve the above object, the present application provides the following technical solutions:

[0008] A kind of soft and rugged road under special vehicle stability robust control method, comprising the following steps:

[0009] S1, obtain driver input, based on reference generation model, combine vehicle kinematics and dynamics principle, generate the reference input of controller;

[0010] S2, construct linear form model as the basic model for control, utilize the real-time online data generated by real vehicle system operation, carry out model error estimation, construct model based on disturbance estimation, realize the real-time correction and compensation of vehicle dynamics model;

[0011] S3, based on the off-road vehicle dynamics model after correction and compensation, respectively through the yaw stability control based on MPC, based on optimization torque distribution, generate control quantity and send to vehicle, realize the stability control of vehicle.

[0012] Optionally, the reference generation model in S1 generates expected yaw angular velocity as reference input according to driver steering wheel angle, in combination with vehicle two-degree-of-freedom model.

[0013] Optionally, vehicle two-degree-of-freedom model considers the lateral motion and yaw motion of vehicle, and the formula is as follows:

[0014] ;

[0015] Wherein, vehicle state, derivative of , vehicle yaw rate, vehicle mass center side slip angle, and respectively front wheel and rear wheel lateral force, and respectively front axle, rear axle to vehicle mass center distance, vehicle rotation inertia around mass center, additional yaw moment, dynamics model, estimated model, input matrix, , vehicle longitudinal velocity.

[0016] Optionally, the specific content of the model error estimation in S2 is:

[0017] 1) Lateral model estimation:

[0018] The vehicle two-degree-of-freedom model is discretized to obtain:

[0019] ;

[0020] The estimator in discrete form is designed as:

[0021] ;

[0022] ;

[0023] The estimation error is:

[0024] ;

[0025] wherein, is the vehicle state at the next time, is the vehicle state at the current time, is the step size of Euler discretization, is the input matrix, is the yaw moment control input at the current time, is the estimated model at the current time, and are intermediate variables at the current and next times, is the estimator gain matrix, is the model estimation value at the current time, is the model estimation error at the current time, is the model estimation error at the next time, is the estimated model at the next time, is the model estimation value at the next time, is a two-dimensional unit matrix;

[0026] 2) Longitudinal disturbance estimation:

[0027] The dynamics equation of the wheel in the rotational degree of freedom is discretized to obtain:

[0028] ;

[0029] The estimator is designed as:

[0030] ;

[0031] ;

[0032] The estimation error is:

[0033] ;

[0034] wherein, is the wheel speed at next time instant, is the wheel speed at current time instant, is the driving torque of each wheel, is the moment of inertia of the wheel, is the estimated model, is the estimator gain, is the driving torque of each wheel at current time instant, is the estimation error of the model.

[0035] Optionally, the specific content of the vehicle dynamics model is:

[0036] The vehicle dynamics model is represented as:

[0037] ;

[0038] wherein, is the total moment acting on each tire, composed of the driver moment and the controller moment , i.e. ; is the tire longitudinal force, is the tire effective radius, is the moment of inertia of the wheel, is the wheel rolling speed, is the derivative of the wheel rolling speed with respect to time, is the estimated wheel rolling dynamics model.

[0039] Optionally, the specific content of the MPC-based yaw stability control in S3 is:

[0040] Upper controller design:

[0041] Based on the estimated disturbance, the lateral two-degree-of-freedom linear model is obtained as follows:

[0042] ;

[0043] wherein,

[0044] ;

[0045] ;

[0046] wherein, is the vehicle state at next time instant, is the vehicle state at current time instant, is the additional yaw torque control input at current time instant, is the estimated model for the current time instant, is the system state matrix, is the input matrix, is the disturbance input matrix for the estimated model, is the vehicle yaw rate, is the vehicle side slip angle, is the additional yaw moment, is the estimated model, is the corresponding state is the estimated model, is the corresponding state is the estimated model, is the Euler discretization time step, is the vehicle moment of inertia about axis;

[0047] The prediction horizon is defined as the future optimization control input sequence , the future vehicle state sequence :

[0048] ;

[0049] where, is the next time instant state predicted based on the current time instant state, is the state after steps predicted based on the current time instant state, is the current time instant additional yaw moment control input to be optimized, is the additional yaw moment control input after the current time instant to be optimized; The prediction equation is then:

[0050] ;

[0051] ;

[0052] ;

[0053] where, is the future vehicle state sequence, is the current vehicle state, , , is the prediction matrix used to predict the future state sequence, is the th power of the system matrix;

[0054] ​The control objective is to track the desired states while suppressing the yaw moment, define the objective function and convert it into a quadratic form as follows:

[0055] ;

[0056] where,

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] where, is the objective function to be optimized, is the reference state sequence, is the weight matrix of the tracking objective, is the weight matrix of the control input, is the prediction horizon, is a diagonal block matrix composed of is a diagonal block matrix composed of is the transpose of the control action sequence, is the control input sequence, is the constant term independent of is the transpose matrix of ; The first element of the optimized sequence

[0062] is the desired yaw moment for the lower longitudinal controller. Optionally, the specific content of the optimized torque distribution is:

[0063] The additional yaw moment generated by the differential torque

[0064] is expressed as:

[0065] ;

[0066] where, is the wheel radius, is the distance between the left and right wheels, is the driving torque of the left front wheel, is the driving torque of the right front wheel, is the driving torque of the left rear wheel, is the driving torque of the right rear wheel, ​for driving torque control input, ;

[0067] The lower controller takes into account both yaw torque tracking and slip ratio suppression, combined with control quantity punishment, and the objective function is defined as follows:

[0068] ;

[0069] wherein, is a wheel desired speed sequence, is a driver desired torque sequence, is an additional yaw torque sequence, is a reference additional yaw torque sequence, is a wheel speed sequence, is a control input sequence, is a motor torque weight matrix, is a weight for tracking additional yaw torque, is a diagonal block matrix composed in a prediction horizon;

[0070] For the lower controller:

[0071] ;

[0072] ;

[0073] ;

[0074] The objective function is converted into the following form:

[0075] ;

[0076] wherein,

[0077] ;

[0078] ;

[0079] ;

[0080] The constraint condition ensures that the total motor torque is within the amplitude:

[0081] ;

[0082] The first group of the optimization sequence is taken as the torque actually acting on the motor;

[0083] wherein, is an additional yaw torque sequence optimized by the upper controller, is the additional yaw torque sequence optimized by the upper controller additional yaw torque after a time step, current optimised at the current motor torque control input after a time step, is the prediction horizon, is a diagonal block matrix composed of, is the control input sequence, is the transpose symbol.

[0084] Compared with the prior art, the technical scheme has the beneficial effects that:

[0085] 1) The model error is estimated in real time by means of online data, the complex working conditions of the soft and rugged road surface can be dynamically adapted, the vehicle dynamics model is more accurate, and a reliable foundation is laid for subsequent control;

[0086] 2) The upper layer generates the expected yaw torque based on the model predictive control, and the lower layer optimizes the torque distribution, taking into account the yaw torque tracking and wheel anti-skid, effectively improving the lateral and longitudinal stability of the special vehicle when driving on the soft and rugged road surface, and enhancing the control reliability of the vehicle under extreme conditions;

[0087] 3) The model error and longitudinal disturbance estimator is designed reasonably, the error can be quickly converged, the controller can respond to the road surface changes and vehicle state changes in time, and the real-time and effectiveness of the control are ensured;

[0088] 4) The lower layer torque distribution controller makes the wheel speed track the vehicle body speed as much as possible, reduces the wheel skidding and the like, improves the smoothness of the vehicle driving, and reasonably restricts the amplitude of the total motor torque, ensuring the stable operation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0089] In order to more clearly illustrate the technical schemes in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0090] Figure 1 A flow chart of a special vehicle stability robust control method on soft and rugged road surface provided by the present application;

[0091] Figure 2 A vehicle two-degree-of-freedom model diagram provided by the present application;

[0092] Figure 3A vehicle dynamics model diagram provided by the present application;

[0093] Figure 4 A vehicle longitudinal speed curve diagram provided by the present application during double lane change (DLC) test under microgravity of soft road surface;

[0094] Figure 5 A vehicle trajectory curve diagram provided by the present application during double lane change (DLC) test under microgravity of soft road surface;

[0095] Figure 6 A double lane change test vehicle yaw angular velocity diagram provided by the present application during DOPC test under microgravity of soft road surface;

[0096] Figure 7 A double lane change test vehicle yaw angular velocity diagram provided by the present application during LQR test under microgravity of soft road surface;

[0097] Figure 8 A double lane change test vehicle yaw angular velocity diagram provided by the present application during Controller off test under microgravity of soft road surface;

[0098] Figure 9 A double lane change test vehicle slip ratio diagram provided by the present application during DOPC test under microgravity of soft road surface;

[0099] Figure 10 A double lane change test vehicle slip ratio diagram provided by the present application during LQR test under microgravity of soft road surface;

[0100] Figure 11 A double lane change test vehicle slip ratio diagram provided by the present application during Controller off test under microgravity of soft road surface. DETAILED DESCRIPTION

[0101] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0102] Referring to Figure 1 As shown in the figure, the present application discloses a special vehicle stability robust control method under soft and rugged road surface, including the following steps:

[0103] S1, obtain the driver input, generate the reference input of the controller based on the reference generation model, and combine the vehicle kinematics and dynamics principles;

[0104] S2, construct a linear form model as a control-oriented base model, use real-time online data generated by real vehicle system operation to perform model error estimation, construct a model based on disturbance estimation, and realize real-time correction and compensation of the vehicle dynamics model;

[0105] S3, based on the off-road vehicle dynamics model after correction and compensation, generate control quantities through MPC-based yaw stability control and optimization-based torque distribution, and send them to the vehicle to realize stability control of the vehicle.

[0106] Further, the reference generation model in S1 generates the expected yaw rate as a reference input according to the driver's steering wheel angle combined with the vehicle two-degree-of-freedom model.

[0107] Further, referring to Figure 2 , the vehicle two-degree-of-freedom model considers the lateral and yaw motion of the vehicle, and the formula is as follows:

[0108] ;

[0109] wherein, is the state of the vehicle, is the derivative of , is the yaw rate of the vehicle, is the center side slip angle, and are the lateral forces of the front and rear wheels, and are the distances from the front and rear axles to the center of mass of the vehicle, is the moment of inertia of the vehicle rotating around the center of mass, is the additional yaw moment, is the dynamics model, is the estimated model, is the input matrix, , is the vehicle longitudinal speed.

[0110] Specifically, the present application directly estimates the unmodeled error using online data, and the formula is as follows:

[0111]

[0112] wherein, and are the lateral forces of the front and rear wheels.

[0113] Further, the specific content of model error estimation in S2 is as follows:

[0114] 1) Lateral model estimation:

[0115] ​The two-degree-of-freedom vehicle model is discretized as:

[0116] ;

[0117] The estimator is designed in discrete form as:

[0118] ;

[0119] ;

[0120] The estimation error is:

[0121] ;

[0122] where, is the vehicle state at next time, is the vehicle state at current time, is the step size of Euler discretization, is the input matrix, is the yaw moment control input at current time, is the estimated model at current time, and are intermediate variables at current and next time, is the estimator gain matrix, is the model estimation value at current time, is the model estimation error at current time, is the model estimation error at next time, is the estimated model at next time, is the model estimation value at next time, is the two-dimensional identity matrix;

[0123] 2) Longitudinal disturbance estimation:

[0124] The dynamics equation of the wheel in the rotational degree of freedom is discretized as:

[0125] ;

[0126] The estimator is designed as:

[0127] ;

[0128] ;

[0129] The estimation error is:

[0130] ;

[0131] where, is the wheel speed at next time, is the wheel speed at the current time, is the drive torque for each wheel, is the moment of inertia of the wheel, is the estimated wheel dynamics model, is the estimator gain, is the drive torque for each wheel at the current time, is the estimation error of the model.

[0132] Further, referring to Figure 3 the vehicle dynamics model is given by:

[0133] The vehicle dynamics model is given by:

[0134] ;

[0135] where, is the total moment acting on each tire, which is composed of the driver moment and the controller moment , i.e. ; is the tire longitudinal force, is the tire effective radius, is the moment of inertia of the wheel, is the wheel roll speed, is the derivative of the wheel roll speed with respect to time, is the estimated wheel roll dynamics model.

[0136] To ensure vehicle stability, appropriate reference sideslip angle and reference yaw rate need to be generated based on the front wheel angle punched by the driver, according to the two-degree-of-freedom model of the vehicle:

[0137] ;

[0138] The following reference quantities can be generated:

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] wherein, is the vehicle mass, is the mass side slip angle, is the longitudinal vehicle speed, is the front wheel steering angle, is the front wheel cornering stiffness, is the rear wheel cornering stiffness, is the front axle track, is the rear axle track, is the yaw rate, is the is the reference yaw rate in the domain, is the is the steering wheel angle in the domain, is the time constant, is the is the sign of the domain, is the vehicle natural frequency, is the damping coefficient, is the wheelbase, is the stability factor, is the yaw rate gain;

[0147] Further, the specific content of the MPC-based yaw stability control in S3 is:

[0148] Upper controller design:

[0149] Based on the estimated disturbance, the lateral two-degree-of-freedom linear model is as follows:

[0150] ;

[0151] wherein,

[0152] ;

[0153] ;

[0154] wherein, is the vehicle state at next time, is the vehicle state at current time, is the additional yaw torque control input at current time, is the estimated model at current time, is the system state matrix, is the input matrix, is the disturbance input matrix of the estimated model, is the vehicle yaw rate, is the mass side slip angle, is the additional yaw moment, is the estimated model, is the corresponding state estimated model, for the corresponding state estimated model, for the time step of Euler discretization, for the rotation inertia of the vehicle around axis;

[0155] prediction horizon is , define the future optimization control input sequence , the future vehicle state sequence :

[0156] ;

[0157] wherein, is the next time state predicted based on the current time state, is the state after steps predicted based on the current time state, is the additional yaw torque control input at the current time to be optimized, is the additional yaw torque control input after time to be optimized; then the prediction equation is:

[0158]

[0159] ;

[0160] ;

[0161] wherein, is the future vehicle state sequence, is the current vehicle state, , , is the prediction matrix for predicting the future state sequence, is the power of the system matrix;

[0162] The control objective is to track the desired state while suppressing the yaw torque, define the objective function and convert it into a quadratic form as follows:

[0163] ;

[0164] wherein,

[0165] ;

[0166] ;

[0167] ​​ ;

[0168] ;

[0169] wherein, is the objective function to be optimized, is the reference state sequence, is the weight matrix of the tracking objective, is the weight matrix of the control input, is the prediction horizon, is a diagonal block matrix composed of is a diagonal block matrix composed of is the transpose of the control action sequence, is the control input sequence, is the constant term independent of is the transpose matrix of ; and and are only intermediate values of the formula calculation, without meaning.

[0170] The first element of the optimization sequence is the desired yaw moment of the lower longitudinal controller.

[0171] Further, the specific content based on the optimized torque distribution is:

[0172] the additional yaw moment generated by the differential torque is expressed as:

[0173] ;

[0174] wherein, is the wheel radius, is the distance between the left and right wheels, is the drive torque of the front left wheel, is the drive torque of the front right wheel, is the drive torque of the rear left wheel, is the drive torque of the rear right wheel, is the drive torque control input, ;

[0175] The lower controller takes into account both yaw moment tracking and slip ratio suppression, combined with control quantity penalty, and the objective function is defined as follows:

[0176] ;

[0177] wherein, is the wheel desired speed sequence, a desired torque sequence for the driver, an additional yaw torque sequence, a reference additional yaw torque sequence, a wheel speed sequence, a control input sequence, a weight matrix for the motor torque, a weight for tracking the additional yaw torque, a a diagonal block matrix composed of

[0178] For the lower controller:

[0179] ;

[0180] ;

[0181] ;

[0182] The objective function is formulated as follows:

[0183] ;

[0184] wherein,

[0185] ;

[0186] ;

[0187] ;

[0188] The constraint condition ensures that the total motor torque is within the amplitude:

[0189] ;

[0190] The first group of the optimization sequence is taken as the torque actually acting on the motor;

[0191] wherein, is the additional yaw torque sequence optimized by the upper controller, is the additional yaw torque optimized by the upper controller at the moment after the time step, the motor torque control input optimized at the moment after the time step, is the control input sequence, is the prediction horizon, is a diagonal block matrix composed of is the control input sequence, is the transpose symbol.

[0192] In one specific embodiment, the present invention is verified using the high-fidelity off-road simulation software Chrono. The driver model built into Chrono is used in the simulation to maintain a specific vehicle speed and follow a given desired path. In the simulation experiment, both the vehicle model and the simulation conditions are built in Chrono.

[0193] The algorithm and corresponding controller of this invention are implemented and verified through co-simulation of software systems.

[0194] 1) Software Selection

[0195] The stability control algorithm, the corresponding controller, and the simulation model of the controlled object controlled by the controller were built using Microsoft Visual Studio 2022 and the high-fidelity off-road vehicle dynamics simulation software Chrono, respectively. The simulation step size was 0.005s, and the control cycle was 0.02s. Chrono is an open-source high-fidelity off-road vehicle dynamics simulation platform. Its main function is to provide a high-fidelity vehicle dynamics model and corresponding simulation conditions. In the simulation experiment, this model replaced the real vehicle as the implementation object of the designed fast solution algorithm. Visual Studio was used for building the controller algorithm, and Chrono's operation also depends on Visual Studio.

[0196] 2) Simulation experiments for verification and comparison

[0197] See Figure 4 , Figure 5 As shown, the effectiveness of the proposed method is demonstrated through co-simulation. The test scenario involves accelerated two-lane change maneuvers on a soft road surface. The proposed method is denoted as DOPC. The comparative method uses a mechanistic approach to build the vehicle model, and the upper-level controller uses linear quadratic control (LQR) to generate additional yaw torque and evenly distribute the motor torque. The method proposed in this invention significantly reduces overshoot in vehicle speed and trajectory tracking.

[0198] See Figure 6 , Figure 7 , Figure 8 As shown, without the controller activated, it is difficult to stabilize the vehicle's yaw rate, and the vehicle may even become unstable. Compared to the uncontrolled situation, the LQR control method improves the stability of the yaw rate; however, during the initial acceleration phase (the first 10 seconds), the LQR control method exhibits significant oscillations in the yaw rate. In contrast, the method of this invention can prevent wheel slippage during the initial rapid acceleration phase, greatly improving vehicle stability.

[0199] SeeFigure 9 、 Figure 10 、 Figure 11 As shown in FIG. 8, FIG. 9 and FIG. 10, it can be seen that, compared with the case without controller assistance and the model-based LQR control method, the method proposed in the present application considers the anti-skid of the wheels, thus reducing the slip rate and greatly improving the longitudinal stability of the vehicle.

[0200] The various embodiments described in this specification are presented by way of example, and each embodiment describes a specific implementation of the general principles described herein. The embodiments are not intended to limit or restrict the general principles in any way. Each embodiment can be used with any other embodiment, to the extent not mutually exclusive. Each embodiment is presented in the interest of reusability.

[0201] The above description of disclosed embodiments provides enough information to enable others skilled in the art to make and use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Accordingly, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A robust control method for the stability of special vehicles on soft and rugged road surfaces, characterized in that, Includes the following steps: S1. Obtain driver input, and based on the reference generation model, combine vehicle kinematics and dynamics principles to generate the controller's reference input; S2. Construct a linear model as the basic model for control, use real-time online data generated by the operation of real vehicle systems to estimate model errors, construct a model based on disturbance estimation, and realize real-time correction and compensation of vehicle dynamics model. S3. Based on the modified and compensated off-road vehicle dynamics model, control quantities are generated and sent to the vehicle through MPC-based yaw stability control and optimized torque distribution, respectively, to achieve vehicle stability control. The specific content of model error estimation in S2 is as follows: 1) Lateral model estimation: Discretizing the two-degree-of-freedom model of the vehicle yields: ； The discrete form estimator is designed as follows: ； ； The estimation errors are: ； in, This indicates the vehicle's status at the next moment. This represents the current vehicle status. Let be the step size for Euler discretization. For the input matrix, This is the yaw torque control input for the current moment. The model estimated at the current moment. and As an intermediate variable between the current and next time step, The estimator gain matrix, The model estimate at the current moment. The model estimation error at the current moment. The model estimation error for the next time step. The model estimated for the next time step. The model estimate for the next time step. It is a two-dimensional identity matrix; 2) Longitudinal disturbance estimation: Discretizing the dynamic equations of the wheel in its rotational degrees of freedom yields: ； The estimator is designed as follows: ； ； The estimation errors are: ； in, This represents the wheel speed at the next moment. The current wheel speed. The driving torque for each wheel, Let be the moment of inertia of the wheel. For the estimated model, For the estimator gain, This represents the driving torque of each wheel at the current moment. This represents the estimation error of the model.

2. The robust stability control method for special vehicles on soft and rugged road surfaces according to claim 1, characterized in that, The reference generation model in S1 generates the desired yaw rate as a reference input based on the driver's steering wheel angle and the vehicle's two-degree-of-freedom model.

3. The robust stability control method for special vehicles on soft and rugged road surfaces according to claim 2, characterized in that, The two-degree-of-freedom model of the vehicle considers the lateral and yaw motions of the vehicle, as shown in the following formula: ； in, Vehicle status. for The derivative, Let yaw rate be the vehicle's angular velocity. The sideslip angle is the angle of the center of mass. and These are the lateral forces on the front and rear wheels, respectively. and These are the distances from the front axle and rear axle to the vehicle's center of gravity, respectively. Let be the moment of inertia of the vehicle rotating about its center of mass. To add yaw moment, For dynamic model, For the estimated model, For the input matrix, For vehicle quality, This represents the vehicle's longitudinal speed.

4. The robust stability control method for special vehicles on soft and rugged road surfaces according to claim 1, characterized in that, The specific content of the vehicle dynamics model is as follows: The vehicle dynamics model is represented as follows: ； in, The total torque acting on each tire, calculated by the driver's torque. and controller torque The sum of them constitutes, that is ; For the longitudinal force of the tire, The effective radius of the tire. Let be the moment of inertia of the wheel. The speed at which the wheel rolls. Let be the derivative of the wheel's rolling speed with respect to time. This is the estimated wheel rolling dynamics model.

5. The robust stability control method for special vehicles on soft and rugged road surfaces according to claim 1, characterized in that, The specific details of the MPC-based yaw stability control in S3 are as follows: Upper-level controller design: Based on the estimated disturbance, the lateral two-degree-of-freedom linear model is obtained as follows: ； in, ； ； in, This indicates the vehicle's status at the next moment. This represents the current vehicle status. This is the additional yaw torque control input for the current moment. The model estimated at the current moment. The system state matrix, For the input matrix, The disturbance input matrix of the estimated model, Let yaw rate be the vehicle's angular velocity. The sideslip angle is the angle of the center of mass. To add yaw moment, For the estimated model, For the corresponding state The estimated model, For the corresponding state The estimated model, Let be the time step of Euler discretization. For vehicles to bypass Moment of inertia of the shaft; Prediction time domain is The future optimization control input sequence is defined as follows. Future vehicle state sequence : ； in, Based on the current state of time The predicted state at the next moment. Based on the current state of time The predicted The state after the step, For the additional yaw torque control input at the current moment to be optimized, For optimization Additional yaw torque control input after the specified time; The prediction equation is then: ； ； in, For the future vehicle state sequence, Current vehicle status , , This is the prediction matrix used to predict future state sequences. For the system matrix Power; The control objective is to track the desired state while suppressing the yaw torque. The objective function is defined and transformed into a quadratic form as follows: ； in, ； ； ； ； in, Let be the objective function to be optimized. For the reference state sequence, The weight matrix for tracking the target, To control the input weight matrix, To predict the time domain, for The diagonal block matrix formed in the prediction time domain for The diagonal block matrix formed in the prediction time domain To control the transpose of the action sequence, To optimize the control input sequence in the future, To and Irrelevant constant terms for The transpose of the matrix; The first element of the optimized sequence The desired yaw torque as the lower-level longitudinal controller.

6. The robust stability control method for special vehicles on soft and rugged road surfaces according to claim 5, characterized in that, The specific details of the optimized torque distribution are as follows: Additional yaw moment generated by differential torque Represented as: ； in, For the wheel radius, The distance between the left and right wheels. This is the drive torque for the left front wheel. This is the driving torque for the right front wheel. This is the drive torque for the left rear wheel. This is the drive torque for the right rear wheel. For drive torque control input, ; The lower-level controller balances yaw torque tracking and slip ratio suppression, and combined with control quantity penalties, the objective function is defined as follows: ； in, For the desired wheel rotation speed sequence, For the driver's desired torque sequence, To add a yaw torque sequence, For reference, additional yaw torque sequence, For wheel speed sequence, To optimize the control input sequence in the future, This is the weighting matrix for the motor torque. To track the weight of the additional yaw torque, for The diagonal block matrix formed in the prediction time domain; For the lower-level controller: ； ； ； The objective function is transformed into the following form: ； in, ； ； ； The constraints ensure that the total torque of the motor is within the specified range: ； The first set of optimized sequences is taken as the actual torque applied to the motor; in, An additional yaw torque sequence optimized for the upper-level controller. for The upper-level controller is optimized at any time. Additional yaw torque after one time step current Optimized in real time Motor torque control input after a time step. To predict the time domain, for The diagonal block matrix formed To optimize the control input sequence in the future, This is the transpose symbol.

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