Robust control method for stability of special vehicle on soft and rugged road surface

Abstract

The invention discloses a robust control method for the stability of a special vehicle on a soft and rugged road surface, and relates to the technical field of vehicle control. Comprising the steps that driver input is acquired, and reference input of a controller is generated based on a reference generation model in combination with vehicle kinematics and dynamics principles; a linear form model is constructed as a control-oriented basic model, model error estimation is performed by using real-time online data generated by operation of a real vehicle system, a model based on interference estimation is constructed, and real-time correction and compensation of a vehicle dynamics model are realized; and on the basis of the corrected and compensated off-road vehicle dynamics model, control quantities are generated and sent to the vehicle through yaw stability control based on MPC and torque distribution based on optimization, and stability control over the vehicle is achieved. The method can dynamically adapt to complex working conditions of soft and rugged road surfaces, so that a vehicle dynamics model is more accurate, and a reliable foundation is laid for subsequent control.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and more specifically to a robust stability control method for special vehicles on soft and rugged road surfaces. Background Technology

[0002] The complex and varied terrain of off-road courses, including rugged, sandy, muddy, steep, rocky, and gully conditions, can lead to dynamic changes in the relationship between the road surface friction coefficient and the wheel-ground interaction. On soft surfaces such as sand and snow, the interaction between the wheel and the ground involves complex mechanical phenomena such as sinking and slippage. On steep slopes or lateral tilting conditions, the vehicle's center of gravity may shift, resulting in a significantly different dynamic response compared to driving on flat roads. Traditional methods struggle to construct accurate vehicle dynamic models, making it difficult to accurately predict vehicle responses, thus further complicating the motion control tasks of off-road vehicles.

[0003] Most existing methods focus only on improving vehicle control performance under a single operating condition, making it difficult to comprehensively cover the needs of various off-road conditions. For example, patent CN11963661A proposes a method and device for off-road vehicle motion control based on model predictive control (MPC), which partially solves the stability problem at high speeds. Although this method considers the physical characteristics of off-road terrain and establishes a terrain dynamics model, the modeling approach based on physical laws is too idealistic and struggles to accurately capture wheel-ground relationships under highly uncertain road conditions, potentially affecting the algorithm's ability to guarantee terrain constraints on rugged or gully surfaces. Furthermore, patent CN119389193A provides a speed control device and method for off-road vehicles on steep slopes. The rule-based method it employs also tends towards idealism, failing to fully consider the uncertainties of road conditions and neglecting wheel slippage and sinking issues on soft or slippery roads, which may lead to decreased vehicle stability.

[0004] In summary, achieving vehicle safety and stability control under off-road conditions faces two core challenges. First, how to address the challenges of modeling wheel-road relationships under complex and variable road conditions, and construct a more accurate vehicle dynamics model to improve stability control performance. Second, there is currently a lack of a comprehensive control scheme that can simultaneously consider both lateral and longitudinal stability. Therefore, developing motion control strategies for off-road vehicles applicable to all conditions is of great significance.

[0005] Therefore, proposing a robust control method for the stability of special vehicles on soft and rugged road surfaces to address the difficulties in existing technologies is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a robust control method for the stability of special vehicles on soft and rugged road surfaces, which is used to solve the technical problems existing in the prior art.

[0007] In order to achieve the above object, the present invention provides the following technical solutions: A robust stability control method for special vehicles on soft and rough road surfaces 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.

[0008] Optionally, 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.

[0009] Optionally, 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, , This represents the vehicle's longitudinal speed.

[0010] Optionally, 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 wheel rolling dynamics 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.

[0011] Optional, 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. For the moment of inertia of the tire, 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.

[0012] Optionally, 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 vehicle's sideslip angle, To add yaw torque control input, For the estimated model, For the corresponding state The estimated model, For the corresponding state The estimated model, For Euler separation; time step of dispersion, 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 control the sequence of actions, For 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.

[0013] Optionally, 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, This is a four-wheel drive torque sequence. This is the weighting matrix for the motor torque. To track the weight of the additional yaw torque, Weights for tracking the desired speed of the wheels; 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 This is a four-wheel drive torque sequence. This is the transpose symbol.

[0014] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a robust control method for the stability of special vehicles on soft and rugged road surfaces, the beneficial effects of which are: 1) By using online data to estimate model errors in real time, it can dynamically adapt to complex working conditions such as soft and rugged road surfaces, making the vehicle dynamics model more accurate and laying a reliable foundation for subsequent control. 2) The upper layer generates the desired yaw torque based on model predictive control, while the lower layer optimizes torque distribution, taking into account both yaw torque tracking and wheel anti-slip, effectively improving the lateral and longitudinal stability of special vehicles when driving on soft and rugged roads, and enhancing the vehicle's handling reliability under extreme conditions. 3) The model error and longitudinal disturbance estimator are reasonably designed and can quickly converge the error, allowing the controller to respond to road changes and vehicle status changes in a timely manner, ensuring the real-time performance and effectiveness of the control. 4) The lower-level torque distribution controller makes the wheel speed follow the vehicle speed as much as possible, reducing wheel slippage and improving the smoothness of vehicle driving. At the same time, it reasonably constrains the total torque amplitude of the motor to ensure the stable operation of the power system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 A flowchart of a robust stability control method for special vehicles on soft and rugged road surfaces provided by the present invention; Figure 2 This is a diagram of a two-degree-of-freedom vehicle model provided by the present invention; Figure 3 The vehicle dynamics model diagram provided by this invention; Figure 4 The longitudinal velocity curve of the vehicle during the double lane change (DLC) test under microgravity on a soft road surface provided by the present invention; Figure 5 The vehicle trajectory curve during the dual lane change (DLC) test under microgravity on a soft road surface provided by this invention; Figure 6 The yaw rate diagram of the double lane change test vehicle during the DOPC test under microgravity on a soft road surface provided by the present invention. Figure 7 The yaw rate diagram of the double lane change test vehicle during the LQR test under microgravity on a soft road surface provided by the present invention. Figure 8 The yaw rate diagram of the double lane change test vehicle during the Controller off test under microgravity on a soft road surface provided by the present invention; Figure 9 The slip ratio diagram of the double lane change test vehicle during the DOPC test under microgravity on a soft road surface provided by the present invention. Figure 10 The slip ratio diagram of a double lane change test vehicle during LQR test under microgravity on a soft road surface provided by the present invention. Figure 11 The slip ratio diagram of the double lane change test vehicle during the controller off test under microgravity on a soft road surface provided by the present invention. Detailed Implementation

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] See Figure 1 As shown, this invention discloses a robust control method for the stability of special vehicles on soft and rugged road surfaces, comprising 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.

[0019] Furthermore, 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.

[0020] Further, see Figure 2 As shown, the two-degree-of-freedom model of the vehicle considers the lateral and yaw motions of the vehicle, and the formulas are as follows: ; 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, , This represents the vehicle's longitudinal speed.

[0021] Specifically, this invention directly uses online data to estimate the unmodeled error, as shown in the following formula:

[0022] in, and These are the lateral forces on the front and rear wheels, respectively.

[0023] Furthermore, 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 wheel rolling dynamics 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.

[0024] Further, see Figure 3 As shown, 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. For the moment of inertia of the tire, 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.

[0025] To ensure vehicle stability, appropriate reference center of gravity sideslip angle and reference yaw rate need to be generated based on the front wheel steering angle input by the driver, according to the vehicle's two-degree-of-freedom model: ; The following reference values ​​can be generated: ; ; ; ; ; ; ; in, For vehicle quality, The sideslip angle is the angle of the center of mass. For longitudinal vehicle speed, For the front wheel steering angle, For the front wheel lateral stiffness, For rear wheel lateral stiffness, This refers to the front axle wheelbase. This refers to the rear axle wheelbase. The yaw rate is angular velocity. for Reference yaw rate within the domain, for The steering wheel angle of the domain, It is a time constant. for Domain change symbol, For the vehicle's inherent frequency, The damping coefficient is... Wheelbase As a stability factor, Gain for yaw rate; Furthermore, 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 vehicle's sideslip angle, To add yaw torque control input, 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 control the sequence of actions, For Irrelevant constant terms for The transpose of the matrix; and It's just an intermediate value calculated by the formula and has no meaning.

[0026] The first element of the optimized sequence The desired yaw torque as the lower-level longitudinal controller.

[0027] Furthermore, 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, This is a sequence of wheel speeds. This is a four-wheel drive torque sequence. This is the weighting matrix for the motor torque. To track the weight of the additional yaw torque, Weights for tracking the desired speed of the wheels; 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 This is a four-wheel drive torque sequence. This is the transpose symbol.

[0028] 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.

[0029] The algorithm and corresponding controller of this invention are implemented and verified through co-simulation of software systems.

[0030] 1) Software Selection

[0031] 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.

[0032] 2) Simulation experiments for verification and comparison

[0033] 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.

[0034] 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.

[0035] See Figure 9 , Figure 10 , Figure 11 As shown, compared with the case without controller assistance and the model-based LQR control method, the method proposed in this invention takes into account the anti-slip of the wheels, thus reducing the slip ratio and greatly improving the longitudinal stability of the vehicle.

[0036] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0037] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not 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.

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 of Let yaw rate be the vehicle's angular velocity. The sideslip angle is the angle of the centroid. 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 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 wheel rolling dynamics 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.

5. 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 the components, i.e. ; For the longitudinal force of the tire, The effective radius of the tire. For the tire's rotational inertia, 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.

6. 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. For vehicle sideslip angle, To add yaw torque control input, 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 control the sequence of actions, 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.

7. The robust stability control method for special vehicles on soft and rugged road surfaces according to claim 6, 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 represents the drive 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, This is a sequence of wheel speeds. This is a four-wheel drive torque sequence. This is the weighting matrix for the motor torque. To track the weight of the additional yaw torque, Weights for tracking the desired speed of the wheels; 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 This is a four-wheel drive torque sequence. This is the transpose symbol.

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