Distributed Drive Vehicle Chassis Cooperative Control Method Based on Variable Steering Characteristics

Abstract

This invention relates to a distributed drive vehicle chassis cooperative control method based on variable steering characteristics, belonging to the field of vehicle dynamics control technology, and includes the following steps: S1: Generate a reference vehicle state based on the target steering characteristics and a two-degree-of-freedom two-wheel vehicle lateral dynamics model; S2: Generate the AFS front wheel steering angle and DYC additional yaw moment based on MPC control; S3: Distribute wheel drive or braking torque based on minimizing tire utilization and the target additional yaw moment. This invention can adjust the steering characteristics of the controlled vehicle without changing the hardware characteristics; this invention is beneficial for setting constraints and improving control accuracy; this invention improves vehicle lateral stability by minimizing tire utilization.

Description

Technical Field

[0001] This invention belongs to the field of vehicle dynamics control technology, and relates to a distributed drive vehicle chassis cooperative control method based on variable steering characteristics. Background Technology

[0002] With the booming development of the automotive industry and the continuous improvement of the social and economic level, people's demands for automobiles have gone beyond the basic function of displacement. The personalized driving experience of vehicles is receiving increasing attention, and the electronic control system in the vehicle chassis domain provides the possibility to meet this further demand: Active Front Steering (AFS) and Direct Yaw Control (DYC), as mainstream technologies in the field of vehicle dynamics control, have been widely used to improve vehicle handling and safety.

[0003] AFS (Adaptive Front Steering) is designed to compensate for understeer or oversteer tendencies by dynamically adjusting the front wheel steering angle. However, the effectiveness of AFS is limited by the mechanical performance limits of the tires, especially on low-friction surfaces or under extreme driving conditions, where the lateral forces on the tires are prone to saturation, leading to AFS control failure.

[0004] DYC (Distributed Yaw Control) generates additional yaw moment by distributing wheel drive or braking forces, effectively suppressing vehicle sideslip and improving trajectory tracking accuracy. However, in vehicles with a centralized drive architecture, DYC strategies that rely excessively on braking intervention may induce longitudinal dynamic oscillations, reducing ride comfort. In contrast, distributed drive systems (where each wheel is independently driven by a hub motor or wheel-side motor) can precisely decouple wheel drive forces, providing greater freedom for DYC control strategies.

[0005] Currently, AFS and DYC systems are mostly used for vehicle stability control, and control strategies for personalized driving are relatively lacking. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a chassis cooperative control system based on variable steering characteristics. The system generates a reference vehicle state through the target steering characteristics, and incorporates AFS steering angle compensation and DYC torque distribution into a unified constraint solution based on a multi-objective optimization framework of Model Predictive Control (MPC). It also introduces tire force utilization rate for wheel drive / braking force distribution, and finally achieves tracking of the reference vehicle state, enabling the controlled vehicle to exhibit the target steering characteristics in order to achieve a personalized driving experience.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A distributed drive vehicle chassis cooperative control method based on variable steering characteristics includes the following steps:

[0009] S1: Generate reference vehicle state based on target steering characteristics and a two-degree-of-freedom two-wheeled vehicle lateral dynamics model;

[0010] S2: Generates AFS front wheel steering angle and DYC additional yaw moment based on MPC control.

[0011] S3: Distribute wheel drive or braking torque based on minimizing tire utilization and the target additional yaw moment.

[0012] Furthermore, step S1, which involves generating a reference vehicle state based on the target steering characteristics using a two-degree-of-freedom two-wheeled vehicle lateral dynamics model, specifically includes:

[0013] S11: Based on Newton's second law, establish the differential equations for the lateral dynamics model of a two-degree-of-freedom two-wheeled vehicle;

[0014] S12: On the differential equation established in step S11, the tire lateral force is replaced by the product of the lateral stiffness and the lateral slip angle, and substituted into the formula for calculating the lateral slip angle.

[0015] S13: In the differential equation modified in step S12, let the lateral velocity of the vehicle be v. u The steady-state response of the two-degree-of-freedom model is obtained by taking the derivative of the vehicle's yaw rate r as 0.

[0016] S14: The understeer gradient K, which characterizes the vehicle's steering characteristics, is expressed as a function of the front and rear tire lateral stiffness, thus obtaining an explicit relationship between lateral stiffness and steering characteristics.

[0017] Furthermore, the differential equation in step S11 is:

[0018]

[0019] Where m is the vehicle mass, I z For vehicle quality, l f l is the distance from the front axle to the center of gravity of the vehicle. r v is the distance from the rear axle to the center of gravity of the vehicle. x v is the longitudinal velocity of the vehicle. y Let r be the lateral velocity of the vehicle, r be the yaw rate of the vehicle, and δ be the lateral velocity of the vehicle. f F is the steering angle of the vehicle's front wheels. yi (i = f, r) represents the lateral force of the front and rear tires;

[0020] In step S12, the lateral force of the tire is written as the lateral stiffness C. α The product of the sideslip angle α is:

[0021]

[0022] The formula for calculating the sideslip angle α is:

[0023]

[0024] The original differential equation can be rewritten as follows:

[0025]

[0026] The steady-state response of the two-degree-of-freedom model described in step S13 is:

[0027]

[0028] Furthermore, step S2, which involves generating the AFS front wheel steering angle and DYC additional yaw moment based on MPC control, specifically includes the following steps:

[0029] S21: Establish the state space of the controlled object based on the differential equations of the two-degree-of-freedom four-wheel vehicle dynamics model;

[0030] S22: Predict the system's state variables and output variables based on the system's current state variables and future control input variables;

[0031] S23: Minimize the weighted quadratic cost function of tracking error and control input, and expand it into matrix form;

[0032] S24: Define the constraints of the input, output, or state variables and explicitly embed them into the optimization problem;

[0033] S25: Solve the optimization problem using quadratic programming to obtain the control input sequence;

[0034] S26: After applying the control input, the system advances to the next moment, rolls the time domain, returns to step S21, and re-executes S21-S25.

[0035] Furthermore, the state space of the controlled object established in step S21 is as follows:

[0036] Based on the two-degree-of-freedom four-wheel vehicle lateral dynamics model, assuming the lateral velocity v y Given the yaw rate r, the following state space can be established:

[0037]

[0038] Where the state vector x = [v y ,r] T , control input vector u = [δ AFS M DYC ], output vector y = [v y ,r]T Let A be the system matrix, B be the control matrix, and C be the output matrix. The expressions for each matrix are as follows:

[0039]

[0040] in

[0041]

[0042]

[0043] Furthermore, in step S22, based on the current system state x(k) and the future control sequence U = [u(k|k), u(k+1|k), ..., u(k+N)], the following steps are performed: c -1|k)], predicting the future N of the system p Step status and output:

[0044]

[0045] y(k+i|k)=Cx(k+i|k),(i=1,2,…,N p )

[0046] Where N p For prediction in the time domain, N c To control the time domain (N) c ≤N p );

[0047] The weighted quadratic cost function that minimizes the tracking error and control input in step S23 is:

[0048]

[0049] Expand into matrix form:

[0050] J = Y T QY+U T RU

[0051] Where Y = [y(k+i|k),…,y(k+N)] p |k)] T To predict the output sequence, and Let r(k+i) be the weight matrix, and r(k+i) be the reference (v) from the two-degree-of-freedom lateral dynamics model of the vehicle. y ,r) trajectory.

[0052] Furthermore, the constraints are defined in step S24 as follows:

[0053] Explicitly embed input, output, or state constraints into the optimization problem:

[0054] Input constraints are determined by actuator limitations:

[0055] u min ≤u(k+i|k)≤u max (i = 0, 1, ..., n) c -1)

[0056] Output or state constraints:

[0057] y min ≤y(k+i|k)≤y max (i = 1, 2, ..., N) p )

[0058] The solution to the optimization problem in step S25, which yields the control input, includes:

[0059] Substituting the prediction model into the objective function, we construct a quadratic programming problem with U as the optimization variable:

[0060]

[0061] Among them, the second-order sensitivity matrix H of the control input to the objective function and the linear driving vector F of the deviation between the reference vector and the current state prediction to the objective function are generated by the system model and the weight matrix, and the constraint matrix G and constraint vector h are constructed by the constraint conditions, as follows:

[0062] Hessian matrix H and linear term vector F:

[0063]

[0064] in, It is a lower triangular matrix with elements of . (when i≥j);

[0065] Constraint matrix G and constraint vector h:

[0066]

[0067] Among them, G u G Δu and G y These represent the control quantity, the rate of change of the control quantity, and the output constraint matrix, respectively. u h Δu h y These are the control quantity, the rate of change of the control quantity, and the constraint vector of the output quantity, respectively.

[0068]

[0069] in, mN c×mN c The identity matrix; Represents the Kronecker product. It is of length N c A vector of all 1s; u max =[u 1,max ,..,u m,max ] Y Similarly, u min ;

[0070]

[0071] Where D is N c-1 ×N c The difference matrix;

[0072]

[0073] Furthermore, step S3, which involves distributing wheel drive or braking torque based on minimizing tire utilization and the target additional yaw moment, specifically includes the following steps:

[0074] S31: Introduce tire utilization rate and rewrite it as an expression of wheel torque, while also introducing the tire vertical force considering load transfer;

[0075] S32: Determine the optimization variable as wheel torque and construct the objective function as the sum of squares of tire utilization rate;

[0076] S33: Define the constraints based on the total drive demand and the additional yaw moment calculated in step S25;

[0077] S34: Solve the optimization problem using quadratic programming to obtain the optimal wheel torque.

[0078] Furthermore, in step S31, the definition of tire utilization rate is introduced as follows:

[0079]

[0080] Where F x For the longitudinal force of the tire, F y For the lateral force of the tire, F z Let μ be the vertical force of the tire and μ be the road adhesion coefficient. This variable characterizes the current utilization of the tire's friction force: the closer the tire utilization rate is to 1, the closer the tire is to its friction limit and the more likely it is to slip.

[0081] Rewrite the above formula as a calculation expression using wheel torque:

[0082]

[0083] Where T iR is the wheel torque, and R is the effective rolling radius of the wheel.

[0084] The expression for the tire vertical load considering load transfer is:

[0085]

[0086] Where g is the acceleration due to gravity, l s Half the wheel track, h g For the height of the vehicle's center of gravity, a x Let a be the longitudinal acceleration of the vehicle. y This refers to the vehicle's lateral acceleration.

[0087] Furthermore, in step S32, the objective function is defined:

[0088] Let the optimization variable be the additional torque T:

[0089] T = [T] fl ,T fr ,T rl ,T rr ] T

[0090] The subscripts fl, fr, rl, and rr represent the front left, front right, rear left, and rear right wheels, respectively.

[0091] Construct the objective function as the sum of squares of tire utilization rates:

[0092]

[0093] The constraints defined in step S33 include:

[0094] The overall driving demand constraint is:

[0095] T fl +T fr +T rl +T rr =T total

[0096] Among them, the total demand driving torque T total Determined by driver demand;

[0097] The additional yaw moment constraint is:

[0098] M DYC =M fl +M fr +M rl +M rr

[0099] Where M fl M fr M rl Mrr The torques generated by each wheel about its center of mass are expressed as follows:

[0100]

[0101] The optimization problem described in step S34 involves real-time calculation to obtain the optimal wheel torque.

[0102] Transform the optimization problem into a standard QP problem:

[0103]

[0104] The constraint matrix G and constraint vector h are constructed from the constraint conditions.

[0105] The advantages and beneficial effects of this invention are as follows:

[0106] 1) This invention designs a two-degree-of-freedom two-wheeled vehicle lateral dynamics reference model. By adjusting the virtual lateral stiffness of its front / rear tires, the steering characteristics of the controlled vehicle can be adjusted without changing the hardware characteristics.

[0107] 2) This invention designs a method for generating AFS rotation angle and DYC additional yaw moment based on MPC control, which is beneficial for setting constraints and improving control accuracy;

[0108] 3) This invention designs a wheel torque distribution method that considers load transfer and minimizes tire utilization, thereby improving the lateral stability of the vehicle by minimizing tire utilization.

[0109] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0110] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0111] Figure 1 This is a logical structure diagram of the distributed drive vehicle chassis cooperative control algorithm based on variable steering characteristics of the present invention;

[0112] Figure 2 This is a schematic diagram of a two-degree-of-freedom two-wheeled vehicle's lateral dynamics model.

[0113] Figure 3 This is a schematic diagram of a two-degree-of-freedom four-wheel wheel lateral dynamics model. Detailed Implementation

[0114] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0115] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0116] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0117] Please see Figures 1-3 This invention provides a distributed drive vehicle chassis cooperative control algorithm based on variable steering characteristics. The method specifically includes the following steps:

[0118] Step S1: Establish a two-degree-of-freedom lateral dynamics model of a two-wheeled vehicle as a reference model (see...) Figure 2 )

[0119] S11: Its differential equation is established as follows:

[0120]

[0121] Where m is the vehicle mass, I z For vehicle quality, l f l is the distance from the front axle to the center of gravity of the vehicle. r v is the distance from the rear axle to the center of gravity of the vehicle. x v is the longitudinal velocity of the vehicle. y Let r be the lateral velocity of the vehicle, r be the yaw rate of the vehicle, and δ be the lateral velocity of the vehicle. f F is the steering angle of the vehicle's front wheels. yi (i = f, r) represents the lateral forces of the front and rear tires.

[0122] S12: Write the lateral force of the tire as the lateral stiffness C. α The product of the sideslip angle α is:

[0123]

[0124] The formula for calculating the sideslip angle α is:

[0125]

[0126] The original differential equation can be rewritten as follows:

[0127]

[0128] S13: In equation (4), let the lateral velocity of the vehicle be v y The steady-state response of this two-degree-of-freedom model can be obtained when the derivative of the vehicle's yaw rate r is zero.

[0129]

[0130] S14: By adjusting the virtual lateral stiffness of the front and rear tires in this reference model, the target steering characteristics characterized by the understeer gradient K can be adjusted. The calculation formula is as follows:

[0131]

[0132] Step S2: Generate AFS front wheel steering angle and DYC additional yaw moment based on MPC control, specifically including the following steps:

[0133] S21: Establish the state space of the controlled object:

[0134] Based on the two-degree-of-freedom four-wheel vehicle lateral dynamics model (such as...) Figure 3 Assuming the lateral velocity v y Given the yaw rate r, the following state space can be established:

[0135]

[0136] Where the state vector x = [v y ,r] T , control input vector u = [δ AFS M DYC ], output vector y = [v y ,r] T Let A be the system matrix, B be the control matrix, and C be the output matrix. The expressions for each matrix are as follows:

[0137]

[0138] in

[0139]

[0140] S22: System State and Output Prediction:

[0141] Based on the current system state x(k) and the future control sequence U=[u(k|k),u(k+1|k),…,u(k+N)] c -1|k)], predicting the future N of the system p Step status and output:

[0142]

[0143] y(k+i|k)=Cx(k+i|k), (i=1,2,…,N p (16)

[0144] Where N p For prediction in the time domain, N c To control the time domain (N) c ≤N p ).

[0145] S23: Construct the optimization objective function:

[0146] The weighted quadratic cost function that minimizes the tracking error and control input is:

[0147]

[0148] Expand into matrix form:

[0149] J = Y T QY+U T RU (18)

[0150] Where Y = [y(k+i|k),…,y(k+N)] p |k)] T To predict the output sequence, and Let r(k+i) be the weight matrix, and r(k+i) be the reference (v) from the two-degree-of-freedom lateral dynamics model of the vehicle. y ,r) trajectory.

[0151] S24: Define constraints:

[0152] Explicitly embed input, output, or state constraints into the optimization problem:

[0153] Input constraints (determined by actuator limitations):

[0154] u min ≤u(k+i|k)≤u max (i = 0, 1, ..., N) c-1) (19)

[0155] Output / State Constraints:

[0156] y min ≤y(k+i|k)≤y max (i = 1, 2, ..., N) p (20)

[0157] S25: Solve the optimization problem to obtain the control input:

[0158] Substituting the prediction model into the objective function, we construct a quadratic programming (QP) problem with U as the optimization variable:

[0159]

[0160] The second-order sensitivity matrix H (Hessian matrix) of the control input to the objective function and the linear driving vector F (linear term vector) of the deviation between the reference vector and the current state prediction to the objective function are generated by the system model and the weight matrix. The constraint matrix G and the constraint vector h are constructed by the constraint conditions, as follows:

[0161] Hessian matrix H and linear term vector F:

[0162]

[0163] in, It is a lower triangular matrix with elements of . (when i≥j);

[0164] Constraint matrix g and constraint vector h:

[0165]

[0166] Among them, G u G Δu and G y These represent the control quantity, the rate of change of the control quantity, and the output constraint matrix, respectively. u h Δu h y These are the control quantity, the rate of change of the control quantity, and the constraint vector of the output quantity, respectively.

[0167]

[0168] in, mN c ×mN c The identity matrix; Represents the Kronecker product. It is of length Nc A vector of all 1s; u max =[u 1,max ,..,u m,max ] T Similarly, u min .

[0169]

[0170] Where D is N c-1 ×N c The difference matrix.

[0171]

[0172] S26: Apply control input and scroll the time domain:

[0173] Take the first control vector of the optimization solution [δ] AFS M DYC The action is applied to the system. When the system reaches time k+1, it returns to step S21 with x(k+1) as the new initial state and repeats the above steps.

[0174] Step S3: The wheel torque distribution algorithm based on minimizing tire utilization and the target yaw moment is as follows:

[0175] S31: Introduces the definition of tire utilization rate as:

[0176]

[0177] Where F x For the longitudinal force of the tire, F y For the lateral force of the tire, F z Let μ be the vertical force of the tire and μ be the road adhesion coefficient. This variable characterizes the current utilization of the tire's friction force: the closer the tire utilization rate is to 1, the closer the tire is to its friction limit and the more likely it is to slip.

[0178] Rewrite the above formula as a calculation expression using wheel torque:

[0179]

[0180] Where T i R is the wheel torque, and R is the effective rolling radius of the wheel.

[0181] The expression for the tire vertical load considering load transfer is:

[0182]

[0183] Where g is the acceleration due to gravity, l s Half the wheel track, h g For the height of the vehicle's center of gravity, ax Let a be the longitudinal acceleration of the vehicle. y This refers to the vehicle's lateral acceleration.

[0184] S32: Define the objective function:

[0185] Let the optimization variable be the additional torque T:

[0186] T = [T] fl ,T fr ,T rl ,T rr ] T (31)

[0187] The subscripts fl, fr, rl, and rr represent the front left, front right, rear left, and rear right wheels, respectively.

[0188] Construct the objective function as the sum of squares of tire utilization rates:

[0189]

[0190] S33: Define constraints:

[0191] The overall driving demand constraint is:

[0192] T fl +T fr +T rl +T rr =T total (33)

[0193] Among them, the total demand driving torque T total Determined by driver demand.

[0194] The additional yaw moment constraint is:

[0195] M DYC =M fl +M fr +M rl +M rr (34)

[0196] Where M fl M fr M rl M rr The torques generated by each wheel about its center of mass are expressed as follows:

[0197]

[0198] S34: Solve the optimization problem and calculate the optimal wheel torque in real time.

[0199] Transform the optimization problem into a standard QP problem:

[0200]

[0201] Among them, the constraint matrix G and the constraint vector h are constructed by the constraint conditions, and their derivation process is shown in equations (24)-(27).

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed drive vehicle chassis cooperative control method based on variable steering characteristics, characterized in that: Includes the following steps: S1: Generate reference vehicle state based on target steering characteristics and a two-degree-of-freedom two-wheeled vehicle lateral dynamics model; S2: Generates AFS front wheel steering angle and DYC additional yaw moment based on MPC control; S3: Distribute wheel drive or braking torque based on minimizing tire utilization and the target additional yaw moment; Step S1, which involves generating a reference vehicle state based on the target steering characteristics using a two-degree-of-freedom two-wheeled vehicle lateral dynamics model, specifically includes: S11: Based on Newton's second law, establish the differential equations for the lateral dynamics model of a two-degree-of-freedom two-wheeled vehicle; S12: On the differential equation established in step S11, the tire lateral force is replaced by the product of the lateral stiffness and the lateral slip angle, and substituted into the formula for calculating the lateral slip angle. S13: In the differential equation modified in step S12, let the vehicle's lateral velocity... and vehicle yaw rate The derivative is 0, thus yielding the steady-state response of the two-degree-of-freedom model; S14: Understeer gradient, which characterizes the vehicle's steering properties Expressed as a function of the lateral stiffness of the front and rear tires, the explicit relationship between lateral stiffness and steering characteristics is obtained; The differential equation mentioned in step S11 is: in, For vehicle quality, For vehicle quality, This is the distance from the front axle of the vehicle to its center of gravity. This is the distance from the rear axle of the vehicle to its center of gravity. For the longitudinal speed of the vehicle, For the vehicle's lateral speed, Let yaw rate be the vehicle's angular velocity. For the steering angle of the vehicle's front wheels, ( i = f , r () represents the lateral force between the front and rear tires; In step S12, the lateral force of the tire is written as the lateral stiffness. Side slip angle The product of is: Side slip angle The formula for calculation is: The original differential equation can be rewritten as follows: The steady-state response of the two-degree-of-freedom model described in step S13 is:

2. The distributed drive vehicle chassis cooperative control method based on variable steering characteristics according to claim 1, characterized in that: Step S2, which generates the AFS front wheel steering angle and DYC additional yaw moment based on MPC control, specifically includes the following steps: S21: Establish the state space of the controlled object based on the differential equations of the two-degree-of-freedom four-wheel vehicle dynamics model; S22: Predict the system's state variables and output variables based on the system's current state variables and future control input variables; S23: Minimize the weighted quadratic cost function of tracking error and control input, and expand it into matrix form; S24: Define the constraints of the input, output, or state variables and explicitly embed them into the optimization problem; S25: Solve the optimization problem using quadratic programming to obtain the control input sequence; S26: After applying the control input, the system advances to the next moment, rolls the time domain, returns to step S21, and re-executes S21-S25.

3. The distributed drive vehicle chassis cooperative control method based on variable steering characteristics according to claim 2, characterized in that: The state space of the controlled object is established in step S21 as follows: Based on the two-degree-of-freedom four-wheel vehicle lateral dynamics model, assuming lateral velocity... and yaw rate It can be seen that the following state space is established: Where the state vector , control input vector Output vector , For the system matrix, For the control matrix, The output matrix is ​​as follows; the expressions for each matrix are as follows: in 4. The distributed drive vehicle chassis cooperative control method based on variable steering characteristics according to claim 2, characterized in that: In step S22, based on the current state of the system and future control sequence Predicting the future Step status and output: in To predict the time domain, To control the time domain ( ); The weighted quadratic cost function that minimizes the tracking error and control input in step S23 is: Expand into matrix form: in To predict the output sequence, and This is the weight matrix. Reference from a two-degree-of-freedom lateral dynamics model of a vehicle Trajectory.

5. The distributed drive vehicle chassis cooperative control method based on variable steering characteristics according to claim 2, characterized in that: The constraints are defined in step S24 as follows: Explicitly embed input, output, or state constraints into the optimization problem: Input constraints are determined by actuator limitations: Output or state constraints: The solution to the optimization problem in step S25, which yields the control input, includes: Substituting the prediction model into the objective function, constructing... To solve a quadratic programming problem with optimized variables: Wherein, the second-order sensitivity matrix of the control input to the objective function The linear driving vector of the objective function, which is the deviation between the reference vector and the current state prediction. Generated from the system model and weight matrix, constraint matrix and constraint vector Constructed from constraints, specifically as follows: Hessian matrix and linear term vectors : in, It is a lower triangular matrix with elements of . (when ); ; constraint matrix and constraint vector : in, , and These are the constraint matrices for the control quantity, the rate of change of the control quantity, and the output quantity, respectively. , , These are the control quantity, the rate of change of the control quantity, and the constraint vector of the output quantity, respectively. in, for The identity matrix; Represents the Kronecker product. It is a length of A vector of all 1s; Similarly ; in, for The difference matrix; 6. The distributed drive vehicle chassis cooperative control method based on variable steering characteristics according to claim 1, characterized in that: Step S3, which involves distributing wheel drive or braking torque based on minimizing tire utilization and the target additional yaw moment, specifically includes the following steps: S31: Introduce tire utilization rate and rewrite it as an expression of wheel torque, while also introducing the tire vertical force considering load transfer; S32: Determine the optimization variable as wheel torque and construct the objective function as the sum of squares of tire utilization rate; S33: Define the constraints based on the total drive demand and the additional yaw moment calculated in step S25; S34: Solve the optimization problem using quadratic programming to obtain the optimal wheel torque.

7. The distributed drive vehicle chassis cooperative control method based on variable steering characteristics according to claim 6, characterized in that: In step S31, the definition of tire utilization rate is introduced as follows: in For the longitudinal force of the tire, For the lateral force of the tire, The vertical force of the tire. The road surface adhesion coefficient represents the current utilization of tire friction: the closer the tire utilization rate is to 1, the closer the tire is to the friction limit and is about to slip. Rewrite the above formula as a calculation expression using wheel torque: in For wheel torque, The effective rolling radius of the wheel; The expression for the tire vertical load considering load transfer is: in It is the acceleration due to gravity. It is half the wheel track. For the height of the vehicle's center of gravity, For the longitudinal acceleration of the vehicle, This refers to the vehicle's lateral acceleration.

8. The distributed drive vehicle chassis cooperative control method based on variable steering characteristics according to claim 6, characterized in that: In step S32, the objective function is defined as follows: Let the optimization variable be the additional torque. : Subscript , , , These represent the front left, front right, rear left, and rear right wheels, respectively. Construct the objective function as the sum of squares of tire utilization rates: The constraints defined in step S33 include: The overall driving demand constraint is: Among them, the driving force of total demand Determined by driver demand; The additional yaw moment constraint is: in , , , The torques generated by each wheel about its center of mass are expressed as follows: The optimization problem described in step S34 involves real-time calculation to obtain the optimal wheel torque. Transform the optimization problem into a standard QP problem: Wherein, the constraint matrix and constraint vector Constructed from constraints.

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