Steering fault-tolerant control strategy suitable for three-axis modular distributed electric drive heavy-duty vehicle

Abstract

The invention discloses a steering fault-tolerant control strategy suitable for a three-axis modular distributed electric drive heavy-duty vehicle. Determining a six-wheel rotation angle of the health system according to the steering multi-model set, and establishing a model; the input quantity is the front wheel turning angle of the three shafts, and the vehicle yaw velocity is solved by combining the longitudinal velocity of the vehicle; parsing driver pedal input as a target longitudinal speed; setting total driving force and total yawing moment control quantity, and obtaining total driving force and total yawing moment by using a model predictive control algorithm; according to the relation between the longitudinal force and the slip rate and between the lateral force and the slip rate of the tire, the lateral force borne by the fault wheel is minimized, the rotating speed and the driving torque of the fault wheel are calculated, and a cost function is set with the tracking error of the total driving force and the total yawing torque and the attachment rate of the healthy wheel as soft constraints; the maximum torque and the minimum torque which can be output by the hub motor serve as hard constraints, and a multi-objective optimization problem is established and solved; and finally issuing the control quantity. According to the method, steering fault-tolerant control of the three-axle six-wheel vehicle is realized.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent chassis control, and in particular relates to a steering fault-tolerant control strategy suitable for a three-axle modular distributed electric drive heavy-duty vehicle. Background Art

[0002] The modular distributed electric drive heavy-duty vehicle adopts a new variable configuration chassis, which has the advantages of flexible configuration and strong scalability. The new configuration chassis contains multiple connectable driving modules, each of which is composed of several composite functional units with independent drive, braking, steering and suspension functions, and has the advantages of flexible configuration and strong scalability. The above-mentioned configuration chassis adopts an independently controllable wheel hub motor drive system, a wire-controlled braking system, a wire-controlled steering system and a wire-controlled suspension system, which doubles the number of controllable actuators and increases the degree of control freedom. The composite functional unit structure adopts the form of a vehicle. When turning, the wheels have no fixed geometric constraints. The maximum turning angle of a single wheel can be increased from ±35° to ±60°, which can effectively reduce the vehicle's turning radius. The new variable configuration chassis decouples mechanical constraints, increases the degree of control freedom, and improves the vehicle's flexibility and maneuverability through coordinated control of each wheel. It can effectively broaden the driving performance boundaries of heavy-duty vehicles, thereby supporting safe and efficient heavy-duty transportation.

[0003] Modular distributed electric drive heavy-duty vehicles raise the boundaries of vehicle performance while also bringing new challenges and problems. The composite functional unit corner modules that use all-wheel independent steering are prone to failure, and there is no mechanical connection between the steering controls of the individual wheels. Once a corner module fails, it will greatly affect the steering stability of the vehicle. Therefore, it is urgent to achieve steering fault tolerance for three-axle modular distributed electric drive heavy-duty vehicles to ensure vehicle steering stability and safety. Therefore, the steering fault-tolerant control strategy for three-axle modular distributed electric drive heavy-duty vehicles mentioned in the present invention has important application value. Summary of the Invention

[0004] Purpose of the invention: The present invention provides a steering fault-tolerant control strategy suitable for a three-axle modular distributed electric drive heavy-duty vehicle, which can be applied to the actual control application of a three-axle modular distributed electric drive heavy-duty vehicle to ensure steering stability when a single-angle module steering motor fails.

[0005] Technical Solution: The present invention provides a steering fault-tolerant control strategy for a three-axle modular distributed electric drive heavy-duty vehicle, comprising the following steps:

[0006] (1) Analysis of motion control instructions for the healthy system: The six-wheel steering angles of the healthy system are determined based on the steering multi-model set, and a two-degree-of-freedom vehicle dynamics model is established that takes into account the longitudinal and lateral forces of the tires. The input of the two-degree-of-freedom model is the front wheel steering angles of the three axes. Combined with the longitudinal velocity of the vehicle, the yaw rate of the vehicle in steady state is solved. At the same time, the driver's pedal input is analyzed as the target longitudinal velocity of the vehicle. Subsequently, two control quantities, total driving force and total yaw moment, are established. The model predictive control algorithm is used to track the reference longitudinal velocity and yaw rate sent from the upper layer to obtain the total driving force and total yaw moment expected by the driver.

[0007] (2) Fault-tolerant control torque redistribution: Based on the relationship between the tire longitudinal force and slip ratio and the lateral force and slip ratio, the lateral force on the faulty wheel is minimized. The speed and driving torque of the faulty wheel are calculated. The tracking error of the total driving force and total yaw torque, as well as the adhesion rate of the healthy wheel, are used as soft constraints to set the cost function. The maximum and minimum torque that the hub motor can output are used as hard constraints. A multi-objective optimization problem is established and solved using an optimization method. Finally, the control variable is issued.

[0008] Furthermore, the implementation process of step (1) is as follows:

[0009] A set of steering multi-models was established offline, and the steering angle of the health system was determined based on this.

[0010] The steering wheel angle input by the driver directly controls the front axle angle, and then the center and rear axle angles are determined by the driver-set center, rear, and front axle angle ratio coefficients and the front axle angle. The relationship is as follows:

[0011]

[0012] Where: λ is the angle ratio coefficient of the center axis, rear axle and front wheel, and d is the steering mode.

[0013] Finally, we get δ f , δ m , δ r , directly sent to the chassis for steering control and serves as the input for the subsequent two-degree-of-freedom model.

[0014] The pedal depth input by the driver is then analyzed. The input accelerator pedal depth percentage is considered as the maximum acceleration percentage, and the desired vehicle speed is obtained by integrating it. The equation is as follows:

[0015]

[0016] In the two-degree-of-freedom model, since the present invention only requires one state variable, namely the steady-state yaw angular velocity, only the lateral direction is modeled, and both the longitudinal force and the lateral force of the tire are taken into consideration.

[0017] The following equation is obtained:

[0018]

[0019] i=f,m,r、j=l,r

[0020] Where: i = f, m, r represent the front axle, middle axle and rear axle respectively; j = l, r represent the left and right wheels respectively; F ijx Indicates the longitudinal force on each tire; F ijy Indicates the lateral force on each tire; l i It represents the distance between the three axles and the center of mass, which is positive in front of the center of mass and negative behind the center of mass; b represents the half wheelbase, which is the same for the front, middle and rear axles.

[0021] The modular distributed electric drive vehicle eliminates the drive shaft and adopts wheel-end hub motor drive. The sensor can directly obtain the driving force of each wheel. After modeling the tire rolling resistance, F ijx ; F ijy Obtained by estimation.

[0022] Analyze the above formula and let After calculation, we can get ω r The steady-state value ω rd .

[0023] So we get the expected value v of the longitudinal velocity and yaw angular velocity of the vehicle center of mass xd and ω rd .

[0024] The following state-space equations are then designed to track the longitudinal velocity and yaw rate of the vehicle's center of mass:

[0025]

[0026] The state quantity to be tracked is the expected value v of the longitudinal velocity and yaw angular velocity of the vehicle center of mass obtained above. xd and ω rd , total longitudinal moment T t -T r -T w and the total yaw moment M t As the overall control input of the system, the maximum torque vector sum that each wheel hub motor can output and the maximum and minimum values of the desired vehicle speed and yaw angular velocity are set as constraints.

[0027] Rolling resistance torque T r The specific modeling formula is as follows:

[0028] T r =fmgr(c1+c2v x )

[0029] Where: r is the tire radius, m is the vehicle mass, g is the acceleration of gravity, f is the road condition coefficient, c1 is the rolling resistance constant coefficient, and c2 is the rolling resistance speed correlation coefficient.

[0030] The air resistance moment is calculated by multiplying the tire radius by the air resistance formula. The specific formula is as follows:

[0031]

[0032] Where: r is the tire radius, ρ is the air density, C d is the air resistance coefficient, A is the frontal area, and v is the vehicle speed.

[0033] To optimize tracking, an MPC model predictive control algorithm is established, with the cost set as the state tracking error and the control increment. The cost function is shown below:

[0034]

[0035] Where: Np is the prediction interval, S is the terminal cost weight, Q is the state weight, and R is the control weight.

[0036] In order to solve the optimization problem, the problem is transformed into a quadratic programming problem and solved using the QP solver to obtain the control quantity T t -T r -T w and M t Control quantity T t -T r -T w Re-add T r 、T w Get T t , and obtain the final control quantity T of the healthy system t and M t , which is the expected total control quantity of the fault system.

[0037] The implementation process of step (2) is as follows:

[0038] The task of step (2) is to minimize the lateral tire force on the faulty wheel and use the driving force of the healthy wheel to differentially compensate for the steering torque missing due to the fault. t Assigned to each wheel.

[0039] Of the tire's longitudinal and lateral forces, lateral force is more critical to steering stability. Therefore, the optimization objective is to minimize the lateral force on the faulty wheel. According to the relationships between longitudinal force and slip ratio, and lateral force and slip ratio, at low slip ratios, longitudinal force is low but lateral force is high; at high slip ratios, lateral force is low but longitudinal force is high. Therefore, the slip ratio of the faulty wheel is set to 0.8, which results in a low lateral force on the faulty wheel.

[0040] The slip rate calculation formula is as follows:

[0041]

[0042] Where: i is f, m, r, a is half the wheelbase, and r is the wheel rolling radius.

[0043] Then the required speed of the faulty wheel is calculated based on the slip ratio, using the following formula:

[0044]

[0045] Where: a x is the vehicle acceleration.

[0046] Finally, according to the relationship between torque and speed Calculate the torque of the faulty wheel.

[0047] The healthy wheel comes at the expense of tracking error and adhesion rate of the vehicle's driving torque and yaw torque, as follows:

[0048]

[0049] Therefore, the cost function is set as follows:

[0050] J=φ1·J1+φ2·J2+φ3·J3

[0051] φ i is the weight coefficient of each cost, J i are the vehicle driving torque, tracking error of yaw moment and adhesion rate of healthy wheels respectively.

[0052] Constraints can be set based on the requirements of the final application, such as the minimum and maximum torque output of each wheel and the maximum output power of the motor. The optimization problem is then transformed into a quadratic programming problem for solution. The resulting torque distribution plan for all six wheels is then distributed to the chassis for control, ultimately achieving fault-tolerant control that ensures steering stability.

[0053] Beneficial effects: Compared with the prior art, the beneficial effects of the present invention are: the present invention is suitable for use in six-wheel independent modular distributed electric drive vehicles, and can achieve fault-tolerant control of steering stability when a single-angle module steering motor fails. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of an embodiment of the present invention;

[0055] Figure 2 is a steering mode included in the steering multi-model set of an embodiment of the present invention;

[0056] Figure 3 2 is a force diagram of a vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described in detail below with reference to the accompanying drawings.

[0058] See Figure 1 (1) Analysis of motion control instructions for the healthy system: The six-wheel steering angles of the healthy system are determined based on the steering multi-model set, and a two-degree-of-freedom vehicle dynamics model considering the longitudinal and lateral forces of the tires is established. The input of the two-degree-of-freedom model is the front wheel steering angles of the three axes. Combined with the longitudinal speed of the vehicle, the yaw rate of the vehicle in steady state is solved. At the same time, the driver's pedal input is analyzed as the target longitudinal speed of the vehicle. Subsequently, two control quantities, total driving force and total yaw moment, are established. The reference longitudinal speed and yaw rate sent down by the upper layer are tracked using the model predictive control algorithm to obtain the total driving force and total yaw moment expected by the driver.

[0059] (2) Fault-tolerant control torque redistribution: Based on the relationship between the tire longitudinal force and slip ratio and the lateral force and slip ratio, the lateral force on the faulty wheel is minimized. The speed and driving torque of the faulty wheel are calculated. The tracking error of the total driving force and total yaw torque, as well as the adhesion rate of the healthy wheel, are used as soft constraints to set the cost function. The maximum and minimum torque that the hub motor can output are used as hard constraints. A multi-objective optimization problem is established and solved using an optimization method. Finally, the control variable is issued.

[0060] Furthermore, the implementation process of step (1) is as follows:

[0061] The steering multi-model set is established offline, and the steering angle of the health system is determined by it. The steering multi-model set contains steering modes such as Figure 2 shown.

[0062] The steering wheel angle input by the driver directly controls the front axle angle, and then the center and rear axle angles are determined by the driver-set center, rear, and front axle angle ratio coefficients and the front axle angle. The relationship is as follows:

[0063]

[0064] Where: λ is the angle ratio coefficient of the center axis, rear axle and front wheel, and d is the steering mode.

[0065] Finally, we get δ f , δ m , δ r , directly sent to the chassis for steering control and serves as the input for the subsequent two-degree-of-freedom model.

[0066] The pedal depth input by the driver is then analyzed. The input accelerator pedal depth percentage is considered as the maximum acceleration percentage, and the desired vehicle speed is obtained by integrating it. The equation is as follows:

[0067]

[0068] In the two-degree-of-freedom model, since the present invention only requires one state variable, the steady-state yaw rate, only the lateral direction is modeled, taking both the longitudinal force and the lateral force of the tire into account. Figure 3 shown.

[0069] The following equation is obtained:

[0070]

[0071] i=f,m,r、j=l,r

[0072] Where: i = f, m, r represent the front axle, middle axle and rear axle respectively; j = l, r represent the left and right wheels respectively; F ijx Indicates the longitudinal force on each tire; F ijy Indicates the lateral force on each tire; l i It represents the distance between the three axles and the center of mass, which is positive in front of the center of mass and negative behind the center of mass; b represents the half wheelbase, which is the same for the front, middle and rear axles.

[0073] The modular distributed electric drive vehicle eliminates the drive shaft and adopts wheel-end hub motor drive. The sensor can directly obtain the driving force of each wheel. After modeling the tire rolling resistance, F ijx ; F ijy Obtained by estimation.

[0074] Analyze the above formula and let After calculation, we can get ω r The steady-state value ω rd .

[0075] So we get the expected value v of the longitudinal velocity and yaw angular velocity of the vehicle center of mass xd and ω rd .

[0076] The following state-space equations are then designed to track the longitudinal velocity and yaw rate of the vehicle's center of mass:

[0077]

[0078] The state quantity to be tracked is the expected value v of the longitudinal velocity and yaw angular velocity of the vehicle center of mass obtained above. xd and ω rd , total longitudinal moment T t -T r-T w and the total yaw moment M t As the overall control input of the system, the maximum torque vector sum that each wheel hub motor can output and the maximum and minimum values of the desired vehicle speed and yaw angular velocity are set as constraints.

[0079] Rolling resistance torque T r The specific modeling formula is as follows:

[0080] T r =fmgr(c1+c2v x )

[0081] Where: r is the tire radius, m is the vehicle mass, g is the acceleration of gravity, f is the road condition coefficient, c1 is the rolling resistance constant coefficient, and c2 is the rolling resistance speed correlation coefficient.

[0082] The air resistance moment is calculated by multiplying the tire radius by the air resistance formula. The specific formula is as follows:

[0083]

[0084] Where: r is the tire radius, ρ is the air density, C d is the air resistance coefficient, A is the frontal area, and v is the vehicle speed.

[0085] To optimize tracking, an MPC model predictive control algorithm is established, with the cost set as the state tracking error and the control increment. The cost function is shown below:

[0086]

[0087] Where: Np is the prediction interval, S is the terminal cost weight, Q is the state weight, and R is the control weight.

[0088] In order to solve the optimization problem, the problem is transformed into a quadratic programming problem and solved using the QP solver to obtain the control quantity T t -T r -T w and M t Control quantity T t -T r -T w Re-add T r 、T w Get T t , and obtain the final control quantity T of the healthy system t and M t , which is the expected total control quantity of the fault system.

[0089] The task of step (2) is to minimize the lateral tire force on the faulty wheel and use the driving force of the healthy wheel to differentially compensate for the steering torque missing due to the fault. t Assigned to each wheel.

[0090] Of the tire's longitudinal and lateral forces, lateral force is more critical to steering stability. Therefore, the optimization objective is to minimize the lateral force on the faulty wheel. According to the relationships between longitudinal force and slip ratio, and lateral force and slip ratio, at low slip ratios, longitudinal force is low but lateral force is high; at high slip ratios, lateral force is low but longitudinal force is high. Therefore, the slip ratio of the faulty wheel is set to 0.8, which results in a low lateral force on the faulty wheel.

[0091] The slip rate calculation formula is as follows:

[0092]

[0093] Where: i is f, m, r, a is half the wheelbase, and r is the wheel rolling radius.

[0094] Then the required speed of the faulty wheel is calculated based on the slip ratio, using the following formula:

[0095]

[0096] Where: a x is the vehicle acceleration.

[0097] Finally, according to the relationship between torque and speed Calculate the torque of the faulty wheel.

[0098] The healthy wheel comes at the expense of tracking error and adhesion rate of the vehicle's driving torque and yaw torque, as follows:

[0099]

[0100] Therefore, the cost function is set as follows:

[0101] J=φ1·J1+φ2·J2+φ3·J3

[0102] φ i is the weight coefficient of each cost, J i are the vehicle driving torque, tracking error of yaw moment and adhesion rate of healthy wheels respectively.

[0103] Constraints can be set based on the requirements of the final application, such as the minimum and maximum torque output of each wheel and the maximum output power of the motor. The optimization problem is then transformed into a quadratic programming problem for solution. The resulting torque distribution plan for all six wheels is then distributed to the chassis for control, ultimately achieving fault-tolerant control that ensures steering stability.

[0104] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as such herein, will not be interpreted in an idealized or overly formal sense.

[0105] The meaning of "and / or" in this application means that both situations where each exists alone or both exist at the same time are included.

[0106] The term “connection” as used in this application may mean a direct connection between components or an indirect connection between components via other components.

[0107] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A steering fault-tolerant control strategy for a three-axle modular distributed electric drive heavy-duty vehicle, characterized in that: The following steps are involved: (1) Determine the six-wheel steering angles of the healthy system based on the steering multi-model set, and establish a two-degree-of-freedom vehicle dynamics model that takes into account the tire longitudinal force and lateral force. The two-degree-of-freedom model inputs the front wheel steering angles of the three axes, and combined with the vehicle longitudinal velocity, the vehicle yaw rate in the steady state is solved. Resolving driver pedal input into a vehicle target longitudinal velocity; Two control variables, total driving force and total yaw moment, are established. The model predictive control algorithm is used to track the reference longitudinal velocity and yaw angular velocity sent from the upper layer to obtain the total driving force and total yaw moment expected by the driver. (2) According to the relationship between the longitudinal force and slip rate of the tire and the lateral force and slip rate, the lateral force on the faulty wheel is minimized, the rotation speed and driving torque of the faulty wheel are calculated, the tracking error of the total driving force and total yaw moment and the adhesion rate of the healthy wheel are used as soft constraints to set the cost function, and the maximum and minimum torque that the hub motor can output are used as hard constraints to establish a multi-objective optimization problem and solve it using the optimization method; Finally, the control quantity is issued.

2. A steering fault-tolerant control strategy for a three-axle modular distributed electric drive heavy-duty vehicle according to claim 1, characterized in that: The implementation process of step (1) is as follows: An offline steering multi-model set is established to determine the steering angle of the healthy system. The steering modes included in the steering multi-model set include: center and rear axle counter-steering, center and rear axle same-direction steering, front wheel steering, and diagonal steering. The steering wheel angle input by the driver directly controls the front axle angle, and then the center and rear axle angles are determined by the driver-set center, rear, and front axle angle ratio coefficients and the front axle angle. The relationship is as follows: Where: λ is the angle ratio coefficient of the center axis, rear axle and front wheel, d is the steering mode; Get the rotation angle δ of the three axes f , δ m , δ r , sent to the chassis for steering control, and also serves as input for the subsequent two-degree-of-freedom model; The pedal depth input by the driver is analyzed, and the input accelerator pedal depth percentage is regarded as the maximum acceleration percentage. The expected vehicle speed is obtained by integrating it. The equation is as follows: Where: v xd is the desired vehicle speed, U is the percentage of pedal depth input by the driver, a max is the maximum acceleration of the vehicle; In the two-degree-of-freedom model, only the lateral direction is modeled, taking both the longitudinal and lateral forces of the tire into account; The following equation is obtained: Where: i = f, m, r represent the front axle, middle axle and rear axle respectively; j = l, r represent the left and right wheels respectively; F ijx Indicates the longitudinal force on each tire; F ijy Indicates the lateral force on each tire; l i represents the distance between the three axles and the center of mass, which is positive in front of the center of mass and negative behind the center of mass; b represents the half wheelbase, which is the same for the front, middle and rear axles; m represents the vehicle mass, v x Represents the longitudinal speed of the vehicle, v y represents the lateral speed of the vehicle, ω r Represents the vehicle's yaw rate, δ ij Represents the turning angle of each wheel; The driving force of each wheel is obtained by the sensor on the hub motor, and the tire rolling resistance is modeled to obtain F ijx ; make After calculation, we get ω r The steady-state value ω rd ; Thus, the expected values of the longitudinal velocity and yaw rate of the vehicle's center of mass v are obtained. xd and ω rd ; Design the state-space equations to track the longitudinal velocity and yaw rate of the vehicle's center of mass: The state quantity to be tracked is the expected value v of the longitudinal velocity and yaw angular velocity of the vehicle center of mass xd and ω rd , total longitudinal moment T t -T r -T w and the total yaw moment M t As the total control input of the system; the maximum torque vector sum that each wheel hub motor can output and the maximum and minimum values of the desired vehicle speed and yaw angular velocity are set as constraints; Iz is the vehicle moment of inertia, m is the vehicle mass, r is the wheel rolling radius, T t is the sum of the longitudinal driving torque, T r is the rolling resistance torque, T w is the air resistance moment, M t is the total yaw moment; Rolling resistance torque T r The specific modeling formula is as follows: T r =fmgr(c1+c2v x ) Where: r is the tire radius, m is the vehicle mass, g is the acceleration of gravity, f is the road condition coefficient, c1 is the rolling resistance constant coefficient, and c2 is the rolling resistance speed correlation coefficient; The air resistance moment is calculated by multiplying the tire radius by the air resistance formula. The specific formula is as follows: Where: r is the tire radius, ρ is the air density, C d is the air resistance coefficient, A is the frontal area, and v is the vehicle speed; An MPC model predictive control algorithm is established, with the cost set as the state tracking error and the control increment. The cost function is as follows: Where: Np is the prediction interval, S is the terminal cost weight, Q is the state weight, and R is the control weight x [k+i|k] is the prediction of the tracking error at time k to time k+i, (Δu) [k+i|k] is the control increment at time k+i calculated at time k; In order to solve the optimization problem, the problem is transformed into a quadratic programming problem and solved using the QP solver to obtain the control quantity T t -T r -T w and M t , control quantity T t -T r -T w Re-add T r 、T w Get T t , and obtain the final control quantity T of the healthy system t and M t , which is the expected total control quantity of the fault system.

3. The steering fault-tolerant control strategy for a three-axle modular distributed electric drive heavy-duty vehicle according to claim 2, characterized in that: The implementation process of step (2) is as follows: The task of step (2) is to minimize the lateral tire force on the faulty wheel and use the driving force of the healthy wheel to differentially compensate for the steering torque missing due to the fault. t Assigned to each wheel; The slip ratio of the faulty wheel is set to 0.8, at which point the faulty wheel has a lower lateral force; The slip rate calculation formula is as follows: Where: V y is the lateral velocity of the vehicle, V x is the longitudinal speed of the vehicle, i represents f, m, and r of the front, middle, and rear axles, a is the half wheelbase, r is the wheel rolling radius, ω is the wheel speed, and δ i is the wheel angle; definition of symbols The required speed of the faulty wheel is calculated based on the slip ratio. The formula is as follows: Where: a x is the vehicle acceleration; According to the relationship between torque and speed Calculate the torque of the faulty wheel; The healthy wheel comes at the expense of tracking error and adhesion rate of the vehicle's driving torque and yaw torque, as follows: Therefore, the cost function is set as follows: J=φ1·J1+φ2·J2+φ3·J3 φ i is the weight coefficient of each cost, J i are the vehicle driving torque, tracking error of yaw moment and adhesion rate of healthy wheels respectively; Constraints are set according to the requirements of the final application, and the optimization problem is converted into a quadratic programming problem for solution. The final torque distribution plan for the six wheels is sent to the chassis for control, ultimately achieving fault-tolerant control that ensures steering stability.

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