Path tracking fault-tolerant control method under failure of braking system of self-driving automobile

Abstract

The invention discloses a path tracking fault-tolerant control method under failure of a braking system of an automatic driving vehicle. The method comprises the following steps: establishing a two-degree-of-freedom vehicle transverse dynamic model and a path tracking model; constructing a multi-point fault model of the brake actuator; calculating a comprehensive fault factor, and generating a time-varying parameter under differential braking by integrating various potential faults of the brake actuator; a robust gain scheduling linear parameter follow-up LPV controller is constructed, a control strategy is adjusted in real time according to changes of comprehensive fault factors, and the stability and safety of the vehicle are ensured when a brake actuator breaks down; a braking distribution scheme is designed based on the fault factors, braking torque is distributed according to the vertical load and the fault state of each wheel, and the maximum braking force constraint is met; according to the controller, the stability and safety of the vehicle can be improved under the condition that various types of faults happen to the online control braking system.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous vehicles, and particularly relates to a path tracking fault-tolerant control method for an autonomous vehicle under the failure of a braking system. Background Art

[0002] Fault-tolerant control technology is a key technology to improve the safety of autonomous vehicles. Its working principle is to integrate mechanisms for fault detection, diagnosis, and response in the control system, enabling the vehicle to continue operating safely in the event of component failures or performance degradation. The emergence of fault-tolerant control technology has significantly improved the reliability and robustness of autonomous vehicles, greatly reducing the risk of accidents caused by system failures.

[0003] The braking system of a vehicle is an important component to ensure the safety of autonomous vehicles. It not only directly affects the braking performance of the vehicle but also impacts the handling stability and path tracking ability of the vehicle. Therefore, maintaining the stability and safety of the vehicle when the braking system fails during normal driving is of great safety significance.

[0004] Currently, researchers have conducted extensive research in this area. Although yaw stability control can be achieved through differential braking (independently controlling the braking torque of four tires), there are still two difficulties that have not been solved. Since the electronic braking system of an autonomous vehicle contains many electromechanical components, due to cost and space limitations, it is difficult to design hardware redundancy for some components. Therefore, the first challenge is how to ensure the stability and safety of an autonomous vehicle when the electronic braking system fails; for the electronic braking system, a direct yaw moment is generally obtained by braking the wheels on the same side. So, the second challenge is to design a fault model of the electronic braking system for controller design. Summary of the Invention

[0005] Object of the Invention: The object of the present invention is to provide a path tracking fault-tolerant control method for an autonomous vehicle under the failure of a braking system. On the premise of fully considering several common faults of the electronic braking system, a multi-point fault model is proposed for the analysis and design of the controller, and then an LPV output feedback controller is designed to adjust the braking force of each wheel to ensure the stability and safety of the vehicle when the braking actuator fails.

[0006] Technical Solution: A path tracking fault-tolerant control method for an autonomous vehicle under the failure of a braking system according to the present invention includes the following steps:

[0007] (1) Establish a two-degree-of-freedom vehicle lateral dynamics model and a path tracking model. The model limits the desired vehicle yaw rate within the range related to the friction coefficient and vehicle speed, and realizes path tracking by controlling the lateral offset and heading error to converge to zero.

[0008] (2) Construct a multi-point fault model for the brake actuator. The model comprehensively considers the possible fault types under differential braking, including failure faults, fixed-level jamming faults, and additive faults.

[0009] (3) Calculate the comprehensive fault factor. By integrating various potential faults of the brake actuator, time-varying parameters are generated under differential braking.

[0010] (4) Construct a robust gain-scheduled linear parameter-varying (LPV) controller and a dynamic output feedback controller. According to the change of the comprehensive fault factor, the control strategy is adjusted in real time to ensure the stability and safety of the vehicle when the brake actuator fails.

[0011] (5) Design a brake force distribution scheme based on the fault factor. According to the vertical load and fault state of each wheel, the braking torque is distributed to meet the maximum braking force constraint.

[0012] Furthermore, in step (1), the vehicle lateral dynamics model and path tracking model include the following steps:

[0013] (11) Describe the vehicle lateral dynamics by the following formula:

[0014]

[0015] where m is the vehicle mass, I z is the moment of inertia, γ is the yaw rate, β is the sideslip angle, M z is the direct yaw moment, l f is the distance from the center of mass to the front axle, l r is the distance from the center of mass to the rear axle, F yf and F yr are the lateral tire forces of the front and rear wheels respectively, v y is the vehicle longitudinal speed, v y is the vehicle lateral speed;

[0016] (12) Calculate the tire slip angle by the following formula:

[0017]

[0018] where δ f is the front wheel steering angle;

[0019] (13) The upper limit of the desired yaw rate is determined by the friction coefficient and vehicle speed, and the formula is as follows:

[0020]

[0021] where |γ d,max | is the upper limit of the desired yaw rate, μ is the friction coefficient, and g is the acceleration due to gravity.

[0022] (14) Convert the model to the state - space form:

[0023]

[0024]

[0025] where \(x = [v y ,\gamma] T ,[u1,u2]=[\delta f ,M z , and C i is the front - and - rear wheel turning stiffness.

[0026] Further, in step (2), the specific construction of the brake actuator multi - point fault model includes:

[0027] (21) Derive the relationship between the braking torque and the actual pressure through the general fault model. The formula is as follows:

[0028]

[0029] where \(P rij is the actual brake cylinder pressure, \(f\) is the friction coefficient between the brake disc and the brake shoe, \(A\) is the area of the brake shoe, \(r w is the effective radius during the braking process, \(P dij is the desired brake cylinder pressure given by the upper - layer controller, and \(\Delta P ij is the unknown disturbance caused by the additive fault; is the fault factor, and

[0030] (22) The wire - controlled braking system fault model is expressed as:

[0031]

[0032] where \(j=\{1,3\}\), \(\lambda\) is the comprehensive fault factor, and \(\Delta M Z is the total yaw moment caused by the additive fault or disturbance:

[0033]

[0034] where \(0\lt\lambda\lt1\), \(\Delta T l =\Delta T fl +\Delta T fr \), \(\Delta T r =\Delta T rl +\Delta T rr .

[0035] Further, in step (4), the construction of the LPV controller includes the following steps:

[0036] (41) Taking the comprehensive fault factor λ as a time-varying parameter, an LPV model is established, and the formula is as follows:

[0037]

[0038] (42) Defining the performance objective through the yaw rate signal error weight function W S , the steering control signal weight function W u1 , and the weight function W u2 of the braking control signal, the formula is as follows:

[0039]

[0040] where A is used to define the maximum steady-state error, T = 0.1, E is used to define a good robustness margin, E = 2, ω0 is the required bandwidth, ω0 = 70 rad / s

[0041]

[0042] where ω1 is the required bandwidth. Considering the steering actuator bandwidth, ω1 = 70 rad / s

[0043]

[0044] where ω2 is the required bandwidth, and considering the braking actuator cut-off frequency, ω2 = 70 rad / s;

[0045] (43) Representing the generalized controlled object in state-space form, the formula is as follows:

[0046] ∑:

[0047] where x is the state parameter vector of the generalized system; is the exogenous input, u is the control input, u = [δ f , M zd ; z = [z1, z2, z3] is the weighted control output vector, where z1 = W s (s)(γ - γ d ), z2 = W u1 δ f , Z3 = W u2 M z ; y is the measured output, y = γ.

[0048] Further, in step (4), the dynamic output feedback controller implementation process is as follows:

[0049] (S41) The controller structure formula is as follows:

[0050] S(λ):

[0051] (S42) Solve the controller parameters through linear matrix inequality LMI, and the formula is as follows:

[0052]

[0053] (S43) Generate a real-time controller based on the convex combination of vertices {λ - , λ +}, and the formula is as follows:

[0054]

[0055] Where S( λ ) and are the solutions of the polyhedron problem evaluated at the vertices.

[0056] Further, in step (5), the specific braking distribution scheme is as follows:

[0057] (51) Allocate the braking force of each wheel according to the fault factor α i , and the formula is as follows:

[0058]

[0059] (52) The maximum braking force is determined by the tire vertical load and the friction coefficient, and the formula is as follows:

[0060] T bij ≤ T ijmax = μR t F zij

[0061] (53) Select the left / right wheel braking strategy according to the vehicle steering state to ensure the minimization of the yaw moment tracking error.

[0062] A path tracking fault-tolerant control system under the failure of the braking system of an autonomous vehicle according to the present invention includes:

[0063] Path tracking module: used to establish a two-degree-of-freedom vehicle lateral dynamics model and a path tracking model, the model limits the desired vehicle yaw rate within the range related to the friction coefficient and vehicle speed, and realizes path tracking by controlling the lateral offset and the heading error to converge to zero;

[0064] Braking actuator multi-point fault module: used to construct a braking actuator multi-point fault model, the model comprehensively considers the possible fault types under differential braking, including failure faults, fixed-level stuck faults, and additive faults;

[0065] Comprehensive fault factor module: used to calculate the comprehensive fault factor, and generate time-varying parameters under differential braking by integrating various potential faults of the braking actuator;

[0066] LPV Controller Module: It is used to construct a robust gain-scheduled linear parameter varying (LPV) controller and a dynamic output feedback controller, and adjust the control strategy in real time according to the change of the comprehensive fault factor to ensure the stability and safety of the vehicle when the brake actuator fails.

[0067] Allocation Module: It is used to design a brake force allocation scheme based on the fault factor, and allocate the braking torque according to the vertical load and fault state of each wheel to meet the maximum braking force constraint.

[0068] An electronic device according to the present invention includes a memory, a processor, and a computer program stored on the memory. When the processor executes the program, the steps of any one of the methods are implemented.

[0069] A computer-readable storage medium according to the present invention stores a computer program, characterized in that when the program is executed by a processor, the steps of any one of the methods are implemented.

[0070] Advantageous Effects: Compared with the prior art, the present invention has the following remarkable advantages: A robust gain-scheduled LPV performance fault-tolerant controller of the present invention solves the problem of vehicle stability fault tolerance control under the failure of the braking system during the driving of an autonomous vehicle; The comprehensive fault factor λ is designed as a time-varying parameter, and the influence brought by various faults is simulated by changing the parameter; The present invention takes into account that active FTC needs to adaptively adjust the controller parameters according to the fault detection result, and passive FTC does not depend on the fault detection result, and adopts a robustness design for a certain type of fault. Therefore, the present invention selects passive FTC with higher real-time performance for design to ensure the efficiency of control. Description of the Drawings

[0071] Figure 1 It is a flowchart of the method of the present invention;

[0072] Figure 2 It is the vehicle dynamics model of the present invention;

[0073] Figure 3 It is the path tracking model diagram of the present invention;

[0074] Figure 4 It is the control system flowchart of the present invention. Detailed Embodiments

[0075] The technical solution of the present invention will be further described below with reference to the drawings.

[0076] Considering the current electro-hydraulic braking system (brake-by-wire) technology of autonomous vehicles, and aiming at the problem of unstable path tracking when the brake actuator fails under the existing technical conditions, the present invention provides a design of a path tracking fault-tolerant controller for autonomous vehicle brake actuators to fail, enabling stability control for various brake actuator failures during the normal driving of autonomous vehicles, thereby ensuring the stability and safety of vehicle driving when the brake actuator fails and improving the safety of the overall intelligent transportation environment.

[0077] As Figure 1 shown, an embodiment of the present invention provides a path tracking fault-tolerant control method for an autonomous vehicle brake system failure, including the following steps:

[0078] S1: Establish a two-degree-of-freedom vehicle lateral dynamics model and a path tracking model, as Figure 2 shown, describing a typical bicycle model to limit the desired vehicle yaw rate within a certain range, and the path tracking model converges both the lateral offset and the heading error of the vehicle to 0.

[0079] Specifically, the vehicle lateral dynamics model is expressed as:

[0080]

[0081] where m is the vehicle mass, I z is the moment of inertia, γ is the yaw rate, β is the sideslip angle, M z is the direct yaw moment, l f is the distance from the center of mass to the front axle, l r is the distance from the center of mass to the rear axle, F yf and F yr are the lateral tire forces of the front and rear wheels respectively, v y is the vehicle longitudinal speed, v y is the vehicle lateral speed, and the lateral forces of the vehicle front and rear wheels are calculated therefrom;

[0082] Assume that the vehicle sideslip angle is very small, then the tire slip angle α i is calculated by the following formula:

[0083]

[0084] where δ f is the front wheel steering angle. Due to the limitation of the maximum friction coefficient μ, there is a certain limitation on the desired vehicle yaw rate:

[0085]

[0086] where, |γ d,max| is the upper limit of the desired yaw rate, μ is the friction coefficient, and g is the acceleration due to gravity. Substituting the above formula into the lateral motion and yaw motion models, with v y and γ as the two states, the state - space model is derived as:

[0087]

[0088]

[0089] where x = [v y , γ] T , [u1, u2] = [δ f , M z , and C i is the front - and - rear wheel steering stiffness.

[0090] The goal of the path - tracking problem is to make the vehicle asymptotically track the desired path. Generally, its control objective is to design a controller to make the path - tracking errors (lateral offset and heading error) stable and converge to zero, with δ f and M z as the control inputs.

[0091] Since the tire dynamic load is required to calculate the maximum tire braking torque, according to the vertical load transfer of each tire, the formula for the vertical load of each tire is:

[0092]

[0093] where, F Z is the wheel vertical load, h is the height from the center of mass to the ground, and d is the wheelbase.

[0094] S2: Design a multi - point fault model for the brake actuator, which comprehensively considers various fault types that may occur in the brake - by - wire system under differential braking conditions, including brake failure faults, fixed - level stuck faults, and additive faults, etc., so as to simulate various brake - actuator faults encountered during vehicle driving.

[0095] Based on the general fault model, define as the fault factor, and can describe four different types of faults:

[0096] a、 and △P = 0, no fault;

[0097] b、 and △P≠0, additive fault;

[0098] c、 △P = 0, or △P≠0, failure fault;

[0099] d、 And △P≠0, fixing the horizontal fault.

[0100] Substitute the relationship between the braking torque and the braking pressure into the general fault model, and we get:

[0101]

[0102] Where P rij is the actual brake cylinder pressure, f is the friction coefficient between the brake disc and the brake shoe, A is the area of the brake shoe, r w is the effective radius during the braking process, P dij is the desired brake cylinder pressure given by the upper controller, and △P ij is the unknown disturbance caused by the additive fault.

[0103] Generally, when the vehicle is in an understeering state, the wheels on the inner side of the vehicle generate the required yaw moment through braking. When the vehicle is in an oversteering state, the wheels on the outer side of the vehicle generate the required yaw moment through braking.

[0104] In summary, the actuator fault model of the brake-by-wire system is expressed as:

[0105]

[0106] Where j = {1, 3}, λ is the comprehensive fault factor, and △M Z is the total yaw moment caused by the additive fault or disturbance:

[0107]

[0108] Where 0 < λ < 1, △T l = △T fl + △T fr and △T r = △T rl + △T rr

[0109] S3: Design a comprehensive fault factor λ and use it as a time-varying parameter to quantify the impact of the brake actuator fault on the vehicle's dynamic performance.

[0110] Specifically, the LPV model can be expressed as:

[0111]

[0112] According to the LPV robust control theory, the weight functions W S of the yaw rate signal error, W u1 of the steering control signal, and W u2 of the braking control signal are designed to describe the performance objectives and actuator limitations.

[0113]

[0114] Where A is used to define the maximum steady-state error, T = 0.1, E is used to define a good robustness margin, E = 2, ω0 is the required bandwidth, ω0 = 70 rad / s

[0115]

[0116] Where ω1 is the required bandwidth. Considering the steering actuator bandwidth, ω1 = 70 rad / s

[0117]

[0118] Where ω2 is the required bandwidth, and considering the brake actuator cut-off frequency, ω2 = 70 rad / s

[0119] The generalized plant is represented as:

[0120]

[0121] Where x is the state parameter vector of the generalized system; is the exogenous input, u is the control input, u = [δ f , M zd ; z = [z1, z2, z3] is the weighted control output vector, where z1 = W s (s)(γ - γ d ), z2 = W u1 δ f , Z3 = W u2 M z ; y is the measured output, y = γ. Matrices B2, C1, and D 12 depend on λ, and this difficulty can be alleviated by pre-filtering the control input:

[0122]

[0123] u = C u x u (12)

[0124] Where A u is stable. Then, the generalized LPV plant can be rewritten as:

[0125]

[0126] S4: Design a dynamic output feedback LPV robust controller for path tracking fault tolerance control when the brake actuator fails. This controller can change the control strategy according to the change of the comprehensive fault factor to ensure the stability and safety of the autonomous vehicle when the brake actuator fails.

[0127] Specifically, design a dynamic output feedback controller, including the comprehensive fault factor:

[0128]

[0129] Combine the above equations (18) and (19) to obtain the closed-loop system as:

[0130]

[0131] By incorporating the bounded real lemma (BRL) into the LPV system, the required LPV robust controller can be obtained. Based on the assumptions and congruent transformations of the LMI system analysis results, a non-conservative LMI can be expressed in equation (21), and the goal is to minimize x of.

[0132]

[0133] After solving the comprehensive LMI, obtain the non-singular matrices M and N that satisfy MN T = I - XY, and the controller can be obtained:

[0134]

[0135] where is defined as the change of the controller variable. By solving the given LMI offline, a feasible solution can be obtained.

[0136] The applied controller S(λ) is a convex combination of the controllers synthesized at the vertices {λ - , λ +}:

[0137]

[0138] where S( λ ) and are the solutions of the polytopic problem evaluated at the vertices.

[0139] S5: The required yaw moment is obtained through the upper-layer fault-tolerant controller. Therefore, a brake distribution scheme needs to be designed based on the fault factors of the four brake actuators.

[0140] Because the quasi-static rotational dynamics of the tire can be expressed as:

[0141] Tbij = R t F xij (18)

[0142] The yaw moment Mz can be expressed as:

[0143]

[0144] Based on the fault factors α of the four brake actuators i , the brake distribution scheme can be expressed by Equation (20). Then, a torque distribution rule between the front and rear wheels is designed based on the degree of fault; for example, if α1 > α2, this means that the degree of fault of the front left brake actuator is greater than that of the rear left actuator. Therefore, we hope that the front left brake actuator can play a greater role, which helps to improve the braking efficiency. Where φ = |γ d | - |γ|.

[0145]

[0146] Specifically, considering the vertical load transfer of each wheel, the maximum braking force of each tire can be calculated by the following formula:

[0147] T bij ≤ T ijmax = μR t F zij (21)

[0148] By changing the braking force of each wheel in real time through the change of the fault factor, it is ensured that the autonomous vehicle can maintain a stable driving state and gradually decelerate to a stop when encountering a brake actuator fault during braking, greatly increasing the driving safety of the vehicle.

Claims

1. A path tracking fault-tolerant control method for an autonomous vehicle under the failure of the braking system, characterized in that, It includes the following steps: (1) Establish a two-degree-of-freedom vehicle lateral dynamics model and a path tracking model. The model limits the desired vehicle yaw rate within the range related to the friction coefficient and vehicle speed, and realizes path tracking by controlling the lateral offset and heading error to converge to zero; (2) Construct a multi-point fault model of the brake actuator. The model synthesizes the possible fault types under differential braking, including failure fault, fixed-level stuck fault, and additive fault; (3) Calculate the comprehensive fault factor. By integrating various potential faults of the brake actuator, time-varying parameters are generated under differential braking; (4) Construct a robust gain-scheduled linear parameter varying (LPV) controller and a dynamic output feedback controller, and adjust the control strategy in real time according to the change of the comprehensive fault factor to ensure the stability and safety of the vehicle when the brake actuator fails; (5) Design a brake distribution scheme based on the fault factor, and distribute the braking torque according to the vertical load and fault state of each wheel to meet the maximum braking force constraint.

2. A path tracking fault-tolerant control method for an autonomous vehicle braking system failure, characterized in that, In step (1), the vehicle lateral dynamics model and the path tracking model include the following steps: (11) Describe the vehicle lateral dynamics by the following formula: where m is the vehicle mass, I z is the moment of inertia, γ is the yaw rate, β is the sideslip angle, M z is the direct yaw moment, l f is the distance from the center of mass to the front axle, l r is the distance from the center of mass to the rear axle, F yf and F yr are the lateral tire forces of the front and rear wheels respectively, v y is the longitudinal vehicle speed, v y is the lateral vehicle speed; (12) Calculate the tire slip angle by the following formula: where δ f is the front-wheel steering angle; (13) The upper limit of the desired yaw rate is determined by the friction coefficient and vehicle speed, and the formula is as follows: where |γ d,max | is the upper limit of the desired yaw rate, μ is the friction coefficient, and g is the acceleration due to gravity (14) Convert the model into the state-space form: where x = [v y , γ] T , [u1, u2] = [δ f , M z , and C i is the front and rear wheel turning stiffness.

3. A path tracking fault-tolerant control method for an autonomous vehicle under the failure of the braking system according to claim 1, characterized in that, In step (2), the specific construction of the multi-point fault model of the brake actuator includes: (21) Deduce the relationship between the braking torque and the actual pressure through the general fault model, and the formula is as follows: where, P rij is the actual brake cylinder pressure, f is the friction coefficient between the brake disc and the brake shoe, A is the area of the brake shoe, r w is the effective radius during the braking process, P dij is the desired brake cylinder pressure given by the upper controller, △P ij is the unknown disturbance caused by the additive fault; is the fault factor, and (22) The fault model of the by-wire braking system is expressed as: where j = {1, 3}, λ is the comprehensive fault factor, and △M Z is the total yaw moment caused by additive faults or disturbances: where \(0 < \lambda < 1\), \(\Delta T\) l =\(\Delta T\) fl +\(\Delta T\) fr , \(\Delta T\) r =\(\Delta T\) rl +\(\Delta T\) rr .

4. A path tracking fault-tolerant control method for an autonomous vehicle under the failure of the braking system according to claim 1, characterized in that In step (4), the construction of the LPV controller includes the following steps: (41) Take the comprehensive fault factor λ as the time-varying parameter and establish an LPV model, and the formula is as follows: (42) Define the performance objective through the yaw rate signal error weight function W S , the steering control signal weight function W u1 , the weight function W of the braking control signal u2 as follows: where A is used to define the maximum steady-state error, T = 0.1, E is used to define a good robust margin, E = 2, ω0 is the required bandwidth, ω0 = 70rad / s where ω1 is the required bandwidth. Considering the steering actuator bandwidth, ω1 = 70rad / s where ω2 is the required bandwidth, and considering the brake actuator cut-off frequency, ω2 = 70rad / s; (43) Express the generalized controlled object in the state-space form, and the formula is as follows: where x is the state parameter vector of the descriptor system; is the exogenous input, u is the control input, u = [δ f , M zd ; z = [z1, z2, z3] is the weighted control output vector, where z1 = W s (s)(γ - γ d ), z2 = W u1 δ f , z3 = W u2 M z ; y is the measured output, y = γ.

5. A path tracking fault-tolerant control method for an autonomous vehicle under the failure of the braking system according to claim 4, characterized in that In step (4), the implementation process of the dynamic output feedback controller also includes the following: (S41) The controller structure formula is as follows: (S42) Solve the controller parameters through the linear matrix inequality (LMI), and the formula is as follows: (S43) Generate a real-time controller based on the convex combination of vertices {λ - , λ +}, and the formula is as follows: where S( λ ) and are solutions of the polyhedral problem evaluated at the vertices.

6. The path tracking fault-tolerant control method under the failure of the braking system of an autonomous vehicle according to claim 4, characterized in that In step (5), the specific brake distribution scheme is as follows: (51) Allocate the braking force of each wheel according to the fault factor α i as follows: (52) The maximum braking force is determined by the tire vertical load and the friction coefficient, and the formula is as follows: T bij ≤T ijmax =μR t F zij ; (53) Select the left / right wheel braking strategy according to the vehicle steering state to ensure the minimization of the yaw moment tracking error.

7. A path tracking fault-tolerant control system for an autonomous vehicle under the failure of the braking system, characterized in that, It includes: Path tracking module: used to establish a two-degree-of-freedom vehicle lateral dynamics model and a path tracking model. The model limits the desired vehicle yaw rate within the range related to the friction coefficient and vehicle speed, and realizes path tracking by controlling the lateral offset and heading error to converge to zero; Brake actuator multi-point fault module: Used to construct a brake actuator multi-point fault model. The model synthesizes the possible fault types under differential braking, including failure faults, fixed-level stuck faults, and additive faults; Comprehensive fault factor module: Used to calculate the comprehensive fault factor. By integrating various potential faults of the brake actuator, time-varying parameters are generated under differential braking; LPV controller module: Used to construct a robust gain-scheduled linear parameter varying (LPV) controller and a dynamic output feedback controller. According to the change of the comprehensive fault factor, the control strategy is adjusted in real time to ensure the stability and safety of the vehicle when the brake actuator fails; Allocation module: Used to design a brake allocation scheme based on the fault factor. The braking torque is allocated according to the vertical load and fault state of each wheel to meet the maximum braking force constraint.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1-6.

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