Real-time motion control system and method for four-wheel steering vehicle with corner module configuration

Abstract

The application is a kind of real-time motion control system and method of four-wheel steering vehicle with corner module configuration. It includes path tracking controller, feed-forward and feedback front and rear axle wheel angle distribution controller and Ackerman four-wheel angle distribution controller. The path tracking controller solves the optimal steering control amount of the corner module configuration intelligent vehicle tracking target trajectory. The feed-forward and feedback front and rear axle wheel angle distribution controller takes the optimal vehicle steering angle calculated by the path tracking controller as the front axle target steering angle, then takes the front axle steering angle as the proportional feed-forward, takes the vehicle yaw rate as the proportional feedback, and calculates the rear axle target steering angle. The Ackerman four-wheel angle distribution controller distributes the front / rear axle target steering angle to the four independent steering wheels of the corner module configuration intelligent vehicle according to the Ackerman steering principle, realizing the steering control of the vehicle. The application reduces the calculation amount of the control system, reduces the control difficulty of the corner module vehicle, improves the control real-time performance, and ensures the driving safety.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent transportation, and particularly relates to a real-time motion control system and method for a four-wheel steering vehicle with an angle module configuration. BACKGROUND

[0002] An intelligent vehicle angle module system integrating four-wheel independent driving, independent steering, independent braking and active suspension can accurately and efficiently perform a vehicle driving task and is the best carrier for an intelligent vehicle. The angle module cancels mechanical connection, reduces a large number of mechanical transmission components, optimizes the overall vehicle layout space, supports independent control of each power unit of the vehicle, has the advantages of large driving force, fast power response, high transmission efficiency, easy software definition and redundant reliable design, and greatly improves the safety, comfort and handling performance of the vehicle. Due to more degrees of freedom, the intelligent vehicle based on the angle module also derives new functions, can realize complex driving trajectory control such as four-wheel steering, U-turn, crabbing, lateral translation and eight-character braking, and is more suitable for crowded urban traffic environment. Making full use of the additional degrees of freedom of the vehicle with the angle module configuration and independently controlling the steering angle of each wheel can improve the responsiveness of the vehicle heading change and make the vehicle have better path tracking performance and handling stability.

[0003] The real-time performance of automatic driving motion control directly affects the safety, driving efficiency and passenger experience of the vehicle and is a crucial link in the automatic driving system. Only with good real-time performance can the vehicle stably and efficiently drive in a complex and changeable road environment. However, in the current four-wheel steering control method, too many steering angle / torque control inputs increase the difficulty of overall vehicle control, resulting in large control system calculation and reduced control real-time performance, which cannot guarantee the safety of automatic driving. It is urgent to research a safe and efficient motion control system for the four-wheel independent steering intelligent vehicle with the angle module configuration. SUMMARY

[0004] The present application aims to provide a real-time motion control system and method for a four-wheel steering vehicle with an angle module configuration, and the technical purpose is to solve the accuracy problem of the intelligent vehicle with the angle module configuration in path tracking and the real-time problem of the chassis control of the vehicle with the angle module configuration.

[0005] The technical solution for achieving the purpose of the present application is a real-time motion control system for a four-wheel steering vehicle with an angle module configuration, which comprises a path tracking controller, a feed-forward-feedback front-rear axle wheel steering angle distribution controller and an Ackerman four-wheel steering angle distribution controller.

[0006] The path tracking controller uses the model predictive control method combined with the vehicle dynamics model to solve the optimal steering angle control quantity, i.e. the optimal target steering angle, for the corner module configuration intelligent vehicle to track the target trajectory; the feedforward-feedback front and rear axle wheel angle distribution controller takes vehicle driving stability as the goal, uses the optimal vehicle steering angle calculated by the path tracking controller as the front axle target steering angle, and then uses the front axle angle as proportional feedforward and the vehicle yaw angular velocity as proportional feedback to calculate the rear axle target steering angle; the Ackerman four-wheel angle distribution controller distributes the target steering angles of the front / rear axles to the four independently steered wheels of the corner module configuration intelligent vehicle based on the Ackerman steering principle to achieve vehicle steering control.

[0007] Furthermore, the model predictive controller of the path tracking controller is as follows:

[0008]

[0009] Where N p is the prediction time domain, N c is the control time domain, t is the current time of the system, Q and R are weight matrices, ρ is the weight coefficient, ε is the relaxation factor, Δu is the control increment, A is the Jacobian matrix of the dynamic model, U min and U max is the control quantity limit value, U t is the control quantity at the current moment, ΔU min and ΔU max is the limit value of the control quantity increment, y hc is the hard constraint output, y hc,max and y hc,min is the hard constraint limit, y sc is the soft constraint output, y sc,min and y sc,max is the soft constraint limit.

[0010] Furthermore, the feedforward-feedback front and rear axle wheel angle distribution controller converts the optimal target angle δ obtained by the path tracking controller into * As the front wheel turning angle δ f , the rear wheel angle is calculated by using the integrated control based on the front wheel angle proportional feedforward and yaw rate proportional feedback, that is, Where k1 is the proportional coefficient of the feedforward link, and k2 is the proportional coefficient of the feedback link. The proportional coefficients of the feedforward and feedback links are as follows:

[0011]

[0012] Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, a is the distance from the vehicle's center of mass to the front axle, b is the distance from the vehicle's center of mass to the rear axle, m is the vehicle's mass, is the vehicle longitudinal speed.

[0013] Furthermore, the Ackerman four-wheel steering angle distribution controller uses the Ackerman steering principle to calculate the front and rear wheel steering angles calculated by the feedforward-feedback front and rear axle wheel steering angle distribution controller as the front wheel average steering angle and the rear wheel average steering angle, respectively. The actual steering angles of the four wheels of the vehicle δ i The front and rear turning angle δ of the single track model f , δ r The relationship between them is as follows: where i = fl, fr, rl, rr, representing the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively:

[0014]

[0015] Where W is the vehicle track and L is the vehicle wheelbase.

[0016] A method for implementing real-time motion control of a four-wheel steering vehicle with an angle module configuration based on the above system comprises the following steps:

[0017] S1: When the vehicle is driving, the path tracking controller receives the reference path instruction and vehicle status information;

[0018] S2: The path tracking controller solves the optimal target turning angle δ of the vehicle based on the reference path command and vehicle state information, combined with the vehicle nonlinear dynamics model and model predictive controller. * , and then the optimal target turning angle δ of the vehicle * Send to the feedforward-feedback front and rear axle wheel angle distribution controller;

[0019] S3: Feedforward-feedback front and rear axle wheel angle distribution controller according to the vehicle's optimal target angle δ * and the vehicle longitudinal velocity V x , yaw angular velocity ω, feedforward feedback control is performed with vehicle driving stability as the goal, and the front and rear axle angles δ are calculated f , δ r , and then send the front and rear axle angles to the Ackerman four-wheel angle distribution controller;

[0020] S4: The Ackerman four-wheel steering angle distribution controller distributes the single-axis steering angle to the left and right wheels according to the front and rear axle angles and the Ackerman steering principle, and obtains the vehicle's four-wheel steering angle δ fr , δ fl , δ rr , δ rl , and then send the four-wheel steering angles to the controlled four-wheel independent steering mechanism;

[0021] S5: The controlled four-wheel independent steering mechanism feeds back the vehicle status information to the path tracking controller and the feedforward-feedback front and rear axle wheel angle distribution controller, repeating steps S1-S4. The vehicle status information is continuously updated until the system determines that the vehicle is in a stopped state.

[0022] Furthermore, the reference path instruction in step S1 includes a reference lateral position Y ref and reference heading angle Vehicle status information includes vehicle lateral and longitudinal speed V x ,V y , vehicle center of mass position coordinates X, Y, vehicle heading angle Yaw angular velocity ω.

[0023] Furthermore, step S2 is specifically as follows:

[0024] Establish a three-degree-of-freedom dynamic model of the vehicle:

[0025]

[0026] Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, C lf is the longitudinal stiffness of the front wheel, C lr is the longitudinal stiffness of the rear wheel, s f is the front wheel slip rate, s r is the rear wheel slip rate, δ f is the front wheel turning angle of the vehicle, a is the distance from the vehicle center of mass to the front axle, b is the distance from the vehicle center of mass to the rear axle, I z is the vehicle's moment of inertia;

[0027] The state quantity of the system is The control quantity of the system is u dyn =δ f , output This results in a nonlinear model:

[0028]

[0029] Discretize and linearize the nonlinear model to obtain a linear time-varying model. Use the control variable increment Δu(k) in the model as a new control variable to construct a model containing Δu(k) u(k) = u(k-1) + Δu(k). and Y as output variables, the following state equation is obtained:

[0030]

[0031] Where,

[0032] By constraining the output and control variables, the model predictive controller for path tracking is obtained as follows:

[0033]

[0034] The path tracking controller calculates the optimal steering angle control value δ * , and then sends the optimal steering angle control value to the feedforward-feedback front and rear axle wheel angle distribution controller.

[0035] Furthermore, step S3 is specifically as follows:

[0036] The feedforward-feedback front and rear axle wheel angle distribution controller uses the optimal angle control quantity solved by the path tracking controller as the front wheel angle δ f , when calculating the rear wheel angle, a comprehensive control based on the front wheel angle proportional feedforward and yaw rate proportional feedback is adopted, that is, Among them, k1 is the proportional coefficient of the feedforward link, and k2 is the proportional coefficient of the feedback link;

[0037] Ideally, the center of mass slip angle of the vehicle when turning should be 0, that is, the vehicle's driving direction is consistent with the vehicle's heading. Based on the definition of the center of mass slip angle β, we can get:

[0038]

[0039] Where x and y are the longitudinal and lateral displacements of the vehicle;

[0040] The two-degree-of-freedom model of a monorail vehicle is as follows:

[0041]

[0042] Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, C lf is the longitudinal stiffness of the front wheel, C lr is the longitudinal stiffness of the rear wheel, is the vehicle heading angle, a is the distance from the vehicle center of mass to the front axle, b is the distance from the vehicle center of mass to the rear axle, I z is the vehicle's moment of inertia;

[0043] Perform Laplace transform on the two-degree-of-freedom model of the monorail vehicle. Substituting this into the equation, we get the response of the vehicle's center of mass sideslip angle to the front wheel turning angle, which is:

[0044]

[0045] Let the above formula be zero, that is, let the coefficient of the s term in the numerator and the constant term be zero, and the proportional coefficients of the feedforward and feedback links are as follows:

[0046]

[0047] Furthermore, step S4 is specifically as follows:

[0048] The Ackerman four-wheel steering angle distribution controller uses the Ackerman steering principle to ensure that the vertical lines of all wheels point to the center of the circle when the vehicle turns. When tire sideways are ignored, the inner and outer wheel angle conditions of pure rolling of the steering wheel and no side slip steering are obtained;

[0049] The front and rear wheel angles calculated by the feedforward-feedback front and rear axle wheel angle distribution controller are used as the front wheel average angle and the rear wheel average angle, respectively. The inner and outer wheel angle conditions are obtained as follows:

[0050]

[0051] From the above formula we can get L a , L b :

[0052]

[0053] Thus, the actual turning angle δ of the four wheels of the vehicle is obtained i , and the front and rear turning angle δ of the single track model f , δ r The relationship between them is:

[0054]

[0055] Where W is the vehicle track and L is the vehicle wheelbase;

[0056] The vehicle's four-wheel steering angle δ fr , δ fl , δ rr , δ rl The four-wheel steering angles are then sent to the controlled four-wheel independent steering mechanism to achieve real-time control of the vehicle.

[0057] Compared with the prior art, the present invention has the following significant advantages:

[0058] The present application discloses a real-time motion control system and method for a four-wheel steering vehicle with an angle module configuration. In the path tracking module, a model predictive control method is used to solve the optimal vehicle target angle. In the vehicle control module, a feedforward-feedback controller is designed to distribute the optimal vehicle angle to the front / rear axles, and an angle distribution controller is designed to distribute the target angles of the front and rear axles to the four independently steered wheels.

[0059] The control system and method described in the present application reduce the amount of control system calculations, lower the difficulty of controlling corner module vehicles, improve the real-time performance of control, and ensure the driving safety of corner module vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of the control method for this application;

[0061] Figure 2 This is a schematic diagram of the Ackerman steering principle for the corner module vehicle;

[0062] Figure 3 The lateral positions of the four-wheel steering and front-wheel steering vehicles under different speed conditions obtained in this application;

[0063] Figure 4 The yaw angular velocity of four-wheel steering and front-wheel steering vehicles under different speed conditions obtained in this application. DETAILED DESCRIPTION

[0064] The present invention is further described in detail below with reference to the accompanying drawings.

[0065] See attached Figure 1 , the real-time motion control system and method of a four-wheel steering vehicle with an angle module configuration described in this application include a path tracking controller, a feedforward-feedback front and rear axle wheel angle distribution controller, and an Ackerman four-wheel angle distribution controller. The path tracking controller uses a model predictive control method combined with a vehicle dynamics model to solve the optimal angle control quantity for the angle module configuration intelligent vehicle to track the target trajectory; the feedforward-feedback front and rear axle wheel angle distribution controller takes vehicle driving stability as the goal, uses the optimal vehicle angle calculated by the path tracking controller as the front axle target angle, and then uses the front axle angle as proportional feedforward and the vehicle yaw angular velocity as proportional feedback to calculate the rear axle target angle; the Ackerman four-wheel angle distribution controller distributes the target angles of the front / rear axles to the four independently steered wheels of the angle module configuration intelligent vehicle according to the Ackerman steering principle to achieve vehicle steering control. The specific control method is as follows:

[0066] (1) The path tracking controller adopts a model predictive control method combined with a vehicle nonlinear dynamic model to solve the optimal steering angle control value for the intelligent vehicle to track the target trajectory, thereby realizing the steering control of the vehicle.

[0067] The vehicle's three-degree-of-freedom dynamic model is expressed as follows:

[0068]

[0069] Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, C lf is the longitudinal stiffness of the front wheel, C lr is the longitudinal stiffness of the rear wheel, s f is the front wheel slip rate, s r is the rear wheel slip rate, δ fis the front wheel turning angle of the vehicle, a is the distance from the vehicle center of mass to the front axle, b is the distance from the vehicle center of mass to the rear axle, I z is the vehicle's moment of inertia. The state quantity of the system is The control quantity of the system is u dyn =δ f , output This results in the following nonlinear model:

[0070]

[0071] The nonlinear model is discretized and linearized to obtain a linear time-varying model. Then the control variable increment Δu(k) in the model is used as a new control variable to construct a new form containing Δu(k) (u(k)=u(k-1)+Δu(k)). and Y as output variables, the following state equation is obtained:

[0072]

[0073] Where,

[0074] The control objective of the path tracking controller is to ensure that the vehicle can quickly and stably track the target trajectory. Considering the center of mass sideslip angle constraints, vehicle adhesion constraints, and tire sideslip angle constraints, the output (yaw angle and longitudinal position) and control variables (front wheel slip angle and front wheel slip angle change) are constrained. The path tracking model predictive controller is as follows:

[0075]

[0076] The path tracking controller calculates the optimal steering angle control value δ * , and then send the control quantity to the feedforward-feedback front and rear axle wheel angle distribution controller.

[0077] (2) The feedforward-feedback front and rear axle wheel angle distribution controller uses the optimal angle control quantity solved by the path tracking controller as the front wheel angle δ f , when calculating the rear wheel angle, a comprehensive control based on the front wheel angle proportional feedforward and yaw rate proportional feedback is adopted, that is, Among them, k1 is the proportional coefficient of the feedforward link, and k2 is the proportional coefficient of the feedback link.

[0078] To improve vehicle stability, ideally, the center of mass slip angle should be 0 when the vehicle turns, that is, the vehicle's direction of travel is consistent with the vehicle's heading. Based on the definition of the center of mass slip angle β, we can get:

[0079]

[0080] Where x and y are the longitudinal and lateral displacements of the vehicle.

[0081] The two-degree-of-freedom model of a monorail vehicle is expressed as follows:

[0082]

[0083] Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, C lf is the longitudinal stiffness of the front wheel, C lr is the longitudinal stiffness of the rear wheel, is the vehicle heading angle, a is the distance from the vehicle center of mass to the front axle, b is the distance from the vehicle center of mass to the rear axle, I z is the vehicle's moment of inertia.

[0084] Perform Laplace transform on the above formula, and Substituting this into the equation, we can get the response of the vehicle's center of mass sideslip angle to the front wheel turning angle, which is:

[0085]

[0086] Let the above formula be zero, that is, let the coefficient of the s term in the numerator and the constant term be zero, and the proportional coefficient values ​​of the feedforward and feedback links are as follows:

[0087]

[0088] The feedforward proportional coefficient, k1, is a constant less than zero, intended to improve the vehicle's maneuverability at low speeds. The feedback proportional coefficient, k2, is a speed-dependent function that adjusts the rear wheel angle proportional coefficient in real time based on state feedback to achieve high-speed stability control.

[0089] (3) The Ackerman four-wheel steering angle distribution controller uses the Ackerman steering principle to make the vertical lines of all wheels point to the center of the circle when the vehicle turns, making the vehicle body's cornering posture more stable and smooth, reducing tire wear and improving driving safety. According to the Ackerman steering principle, when the tire side deviation is ignored, the condition for achieving pure rolling of the steering wheel and no side slip steering is that the inner and outer wheel angles have the following Figure 2 The ideal relationship shown.

[0090] In order to achieve a reasonable distribution of the four-wheel independent steering angle, the front and rear wheel angles calculated by the feedforward-feedback front and rear axle wheel angle distribution controller are used as the front wheel average angle and the rear wheel average angle, respectively. Figure 2 We can get:

[0091]

[0092] From the above formula we can get L a , Lb :

[0093]

[0094] From this, the actual turning angle δ of the four wheels of the vehicle can be deduced i (i = fl, fr, rl, rr, representing the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively) and the front and rear turning angles (δ f , δ r ) is:

[0095]

[0096] Where W is the vehicle track and L is the vehicle wheelbase.

[0097] The Ackerman four-wheel steering angle distribution controller allocates the vehicle's four-wheel steering angles δ fr , δ fl , δ rr , δ rl The information is sent to the controlled angle module four-wheel steering vehicle to achieve real-time control of the vehicle. The angle module vehicle then feeds back its own driving status to the path tracking controller and the feedforward-feedback front and rear axle wheel angle distribution controller.

[0098] During the entire real-time motion control system triggering process, the above process is continuously running and the information of each state parameter is continuously updated until the system considers that the vehicle is in a stopped state.

[0099] Example

[0100] The following Simulink / CarSim co-simulation environment is set up to verify whether the four-wheel steering motion controller can improve the vehicle's stability and tracking performance. The vehicle is set to track a double lane change (with a road adhesion coefficient of 0.8) at speeds of 60 km / h, 90 km / h, and 120 km / h, respectively. The four-wheel steering vehicle is compared with a front-wheel steering vehicle.

[0101] Attachment Figure 3 and attached Figure 4 The simulation results for the lateral position and yaw angle of a four-wheel steering vehicle and a front-wheel steering vehicle at different speeds are shown below. The simulation results show that at low speeds, both the four-wheel steering and front-wheel steering vehicles can track the reference trajectory well. However, as speed increases, the front-wheel steering vehicle exhibits oscillations while tracking the path and completely loses control at 120 km / h. The four-wheel steering vehicle, on the other hand, maintains good tracking performance even at high speeds.

[0102] The above are exemplary embodiments of the present application, and the scope of protection of the present application is defined by the claims and their equivalents. Those skilled in the art should understand that the present application is not limited by the above examples. The above examples and descriptions are merely illustrative of the principles of the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time motion control system for a four-wheel steering vehicle with an angle module configuration, characterized in that: Including path tracking controller, feedforward-feedback front and rear axle wheel angle distribution controller and Ackerman four-wheel angle distribution controller; The path tracking controller uses a model predictive control method combined with a vehicle dynamics model to solve the optimal steering angle control variable, i.e., the optimal target steering angle, for the corner module intelligent vehicle to track the target trajectory. The feedforward-feedback front and rear axle wheel angle distribution controller takes vehicle driving stability as the goal, uses the optimal vehicle steering angle calculated by the path tracking controller as the front axle angle, and then uses the front axle angle as proportional feedforward and the vehicle's yaw rate as proportional feedback to calculate the rear axle angle. The Ackerman four-wheel angle distribution controller distributes the front and rear axle angles to the four independently steered wheels of the corner module intelligent vehicle based on the Ackerman steering principle to achieve vehicle steering control. The model predictive controller of the path tracking controller is as follows: Where N p is the prediction time domain, N c is the control time domain, t is the current time of the system, Q and R are weight matrices, ρ is the weight coefficient, ε is the relaxation factor, Δu is the control increment, A is the Jacobian matrix of the dynamic model, U min and U max is the control quantity limit value, U t is the control quantity at the current moment, ΔU min and ΔU max is the limit value of the control quantity increment, y hc is the hard constraint output, y hc,max and y hc,min is the hard constraint limit, y sc is the soft constraint output, y sc,min and y sc,max is the soft constraint limit value; The feedforward-feedback front and rear axle wheel angle distribution controller converts the optimal target angle δ solved by the path tracking controller into * As the front axle angle δ f , the rear axle angle is calculated using a comprehensive control based on the front wheel angle proportional feedforward and yaw rate proportional feedback, that is, Where k1 is the proportional coefficient of the feedforward link, and k2 is the proportional coefficient of the feedback link. The proportional coefficients of the feedforward and feedback links are as follows: Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, a is the distance from the vehicle's center of mass to the front axle, b is the distance from the vehicle's center of mass to the rear axle, m is the vehicle's mass, is the vehicle longitudinal speed.

2. The system according to claim 1, wherein: The Ackerman four-wheel steering angle distribution controller uses the Ackerman steering principle to calculate the front and rear wheel steering angles calculated by the feedforward-feedback front and rear axle wheel steering angle distribution controller as the front wheel average steering angle and the rear wheel average steering angle, respectively. The actual steering angles of the four wheels of the vehicle δ i The front and rear turning angle δ of the single track model f , δ r The relationship between them is as follows: where i = fl, fr, rl, rr, representing the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively: Where W is the vehicle track and L is the vehicle wheelbase.

3. A method for implementing real-time motion control of a four-wheel steering vehicle with an angle module configuration based on the system according to any one of claims 1-2, characterized in that: The steps include: S1: When the vehicle is driving, the path tracking controller receives the reference path instruction and vehicle status information; S2: The path tracking controller solves the optimal target turning angle δ of the vehicle based on the reference path command and vehicle state information, combined with the vehicle nonlinear dynamics model and model predictive controller. * , and then the optimal target turning angle δ of the vehicle * Send to the feedforward-feedback front and rear axle wheel angle distribution controller; S3: Feedforward-feedback front and rear axle wheel angle distribution controller according to the vehicle's optimal target angle δ * and the vehicle longitudinal velocity V x , yaw angular velocity ω, feedforward feedback control is performed with vehicle driving stability as the goal, and the front and rear axle angles δ are calculated f , δ r , and then send the front and rear axle angles to the Ackerman four-wheel angle distribution controller; S4: Ackerman four-wheel steering angle distribution controller distributes the single-axis steering angle to the left and right wheels according to the front and rear axle angles and the Ackerman steering principle, and obtains the vehicle's four-wheel steering angle δ fr , δ fl , δ rr , δ rl , and then send the four-wheel steering angles to the controlled four-wheel independent steering mechanism; S5: The controlled four-wheel independent steering mechanism feeds back the vehicle status information to the path tracking controller and the feedforward-feedback front and rear axle wheel angle distribution controller, repeating steps S1-S4. The vehicle status information is continuously updated until the system determines that the vehicle is in a stopped state.

4. The method according to claim 3, characterized in that The reference path instruction in step S1 includes the reference lateral position Y ref and reference heading angle Vehicle status information includes vehicle lateral and longitudinal speed V x ,V y , vehicle center of mass position coordinates X, Y, vehicle heading angle Yaw angular velocity ω.

5. The method according to claim 4, characterized in that Step S2 is specifically as follows: Establish a three-degree-of-freedom dynamic model of the vehicle: Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, C lf is the longitudinal stiffness of the front wheel, C lr is the longitudinal stiffness of the rear wheel, s f is the front wheel slip rate, s r is the rear wheel slip rate, δ f is the front wheel turning angle of the vehicle, a is the distance from the vehicle center of mass to the front axle, b is the distance from the vehicle center of mass to the rear axle, I z is the vehicle's moment of inertia; The state quantity of the system is The control quantity of the system is u dyn =δ f , output This results in a nonlinear model: Discretize and linearize the nonlinear model to obtain a linear time-varying model. Use the control variable increment Δu(k) in the model as a new control variable to construct a model containing Δu(k) u(k) = u(k-1) + Δu(k). and Y as output variables, the following state equation is obtained: Where, By constraining the output and control variables, the model predictive controller for path tracking is obtained as follows: The path tracking controller calculates the optimal steering angle control value δ * , and then sends the optimal steering angle control value to the feedforward-feedback front and rear axle wheel angle distribution controller.

6. The method according to claim 5, characterized in that Step S3 is specifically as follows: The feedforward-feedback front and rear axle wheel angle distribution controller uses the optimal angle control quantity solved by the path tracking controller as the front wheel angle δ f , when calculating the rear wheel angle, a comprehensive control based on the front wheel angle proportional feedforward and yaw rate proportional feedback is adopted, that is, Among them, k1 is the proportional coefficient of the feedforward link, and k2 is the proportional coefficient of the feedback link; Ideally, the center of mass slip angle of the vehicle when turning should be 0, that is, the vehicle's driving direction is consistent with the vehicle's heading. Based on the definition of the center of mass slip angle β, we can get: Where x and y are the longitudinal and lateral displacements of the vehicle; The two-degree-of-freedom model of a monorail vehicle is as follows: Where C cf is the cornering stiffness of the front wheel, C cr is the cornering stiffness of the rear wheel, C lf is the longitudinal stiffness of the front wheel, C lr is the longitudinal stiffness of the rear wheel, is the vehicle heading angle, a is the distance from the vehicle center of mass to the front axle, b is the distance from the vehicle center of mass to the rear axle, I z is the vehicle's moment of inertia; Perform Laplace transform on the two-degree-of-freedom model of the monorail vehicle. Substituting this into the equation, we get the response of the vehicle's center of mass sideslip angle to the front wheel turning angle, which is: Let the above formula be zero, that is, let the coefficient of the s term in the numerator and the constant term be zero, and the proportional coefficients of the feedforward and feedback links are as follows:

7. The method according to claim 6, characterized in that Step S4 is specifically as follows: The Ackerman four-wheel steering angle distribution controller uses the Ackerman steering principle to ensure that the vertical lines of all wheels point to the center of the circle when the vehicle turns. When tire sideways are ignored, the inner and outer wheel angle conditions of pure rolling of the steering wheel and no side slip steering are obtained; The front and rear wheel angles calculated by the feedforward-feedback front and rear axle wheel angle distribution controller are used as the front wheel average angle and the rear wheel average angle, respectively. The inner and outer wheel angle conditions are obtained as follows: From the above formula we can get L a , L b : Thus, the actual turning angles of the four wheels of the vehicle are obtained i , and the front and rear turning angle δ of the single track model f , δ r The relationship between them is: Where W is the vehicle track and L is the vehicle wheelbase; The vehicle's four-wheel steering angle δ fr , δ fl , δ rr , δ rl The four-wheel steering angles are then sent to the controlled four-wheel independent steering mechanism to achieve real-time control of the vehicle.

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