A front wheel-differential cooperative steering control method and system of a multi-axle distributed drive unmanned vehicle

Abstract

A front wheel-differential cooperative steering control method and system of a multi-axle distributed drive unmanned vehicle, the main steps of which include: obtaining the vehicle state and the desired path; based on the quadratic differential game algorithm of Nash equilibrium theory, calculating the optimal front wheel steering angle and differential steering yaw moment in the path tracking process of the unmanned vehicle to reduce the turning radius and increase flexibility; identifying the stability of the vehicle based on the phase plane of the center of mass side slip angle; using a weight control strategy based on fuzzy rules, the differential steering yaw moment and stability control yaw moment are coordinated and distributed according to the current stability of the vehicle. This method can effectively solve the balanced control of front wheel steering-differential steering and the coordinated control of path tracking performance-stability, and is suitable for multi-axle distributed drive unmanned vehicles that need to consider path tracking accuracy and driving stability.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a front-wheel-differential coordinated steering method and system for a multi-axle distributed drive autonomous vehicle. Specifically, it relates to the balanced control of front-wheel steering and differential steering and the coordinated control of path tracking performance and stability, and is applicable to multi-axle distributed drive autonomous vehicles that need to balance path tracking accuracy and driving stability. Background Technology

[0002] Improving path tracking performance in multi-axis distributed-drive autonomous vehicles relies on the coordinated control of front-wheel steering and differential steering. Front-wheel steering provides the vehicle with basic steering capabilities, while differential steering significantly improves maneuverability and reduces turning radius. The combination of the two can balance steering agility and trajectory tracking accuracy. However, a steering conflict exists between the two: front-wheel steering controls the vehicle's direction through the steering mechanism, while differential steering generates yaw moment through the speed difference between the left and right wheels to assist steering. Although this improves vehicle agility, it easily interferes with the steering commands of front-wheel steering, disrupting the vehicle's dynamic balance and leading to reduced path tracking performance.

[0003] Existing technologies have failed to effectively resolve the control conflict between front-wheel steering and differential steering, and lack an adjustment mechanism between differential steering yaw moment and stability control yaw moment that matches the current state of the vehicle. Specifically, they have the following shortcomings: ① Under stable driving conditions, existing technologies lack coordinated control logic for front wheel steering and differential steering, and cannot maximize the synergistic effect of the two. ② When the vehicle is in an asymptotically stable state between stability and instability, the existing technology lacks a weighted coordination strategy for the differential steering yaw moment and the yaw moment required for stability control, making it difficult to balance the dual requirements of vehicle agility and driving stability. ③ In the case of vehicle instability, existing technology cannot cancel the differential steering yaw moment and switch to a yaw moment mode aimed at stability. This can easily lead to loss of vehicle control or failure of path tracking due to the conflict of the above control quantities. Summary of the Invention

[0004] This disclosure provides a front-wheel-differential cooperative steering method and system for a multi-axle distributed drive autonomous vehicle, used to achieve path tracking and stability coordination control of the multi-axle distributed drive autonomous vehicle.

[0005] This method is based on a quadratic differential game algorithm of Nash equilibrium theory to determine the front wheel steering angle and differential steering yaw moment during the path tracking process of autonomous vehicles, with the goal of reducing the turning radius and increasing flexibility. Based on the phase plane of the center of gravity sideslip angle, the vehicle's stability is accurately identified, and fuzzy rules are set to assign weights to the differential steering yaw moment aimed at increasing agility and the yaw moment aimed at increasing stability: ① When the vehicle is stable, the focus is on improving path tracking accuracy. At this time, the differential steering yaw torque is transmitted to the actuator to make the vehicle more flexible and improve the vehicle's path tracking accuracy. ② When the system is in a state of asymptotic stability between stability and instability, it coordinates the control of differential steering yaw moment with tracking accuracy as the objective and yaw moment with stability as the objective. ③ When in an unstable state, prioritize vehicle driving safety, distribute yaw moment with stability as the goal, so that the vehicle tracks its ideal stable state, and reduce or even avoid safety problems caused by instability.

[0006] Specifically, the front-wheel-differential cooperative steering control method for this multi-axle distributed drive autonomous vehicle mainly includes the following steps: S1, obtain vehicle status and desired path; S2, a quadratic differential game algorithm based on Nash equilibrium theory, calculates the optimal front wheel steering angle and differential steering yaw moment during the path tracking process of an autonomous vehicle with the goal of reducing the turning radius and increasing flexibility. S3 identifies vehicle stability based on the phase plane of the centroid sideslip angle; S4 employs a weighted control strategy based on fuzzy rules to coordinate and distribute the differential steering yaw moment and the stability control yaw moment according to the vehicle's current stability.

[0007] Furthermore, step S1 specifically includes: Acquire vehicle dynamics and road surface adhesion coefficient information; simultaneously receive the desired path generated by the autonomous driving path planning module; The vehicle dynamics states include: vehicle speed, wheel speed, sideslip angle, sideslip velocity, yaw rate, road adhesion coefficient, longitudinal acceleration, and lateral acceleration. The desired path is generated by the autonomous driving path planning module based on environmental perception information and includes path coordinates, curvature, and curvature change rate parameters.

[0008] Furthermore, step S2 specifically includes: Determine initial performance indicators: With the goal of "improving flexibility + ensuring path tracking accuracy", output the vehicle's front wheel steering angle and differential steering yaw moment; Using a quadratic differential game strategy based on Nash equilibrium theory, and taking path tracking accuracy as the performance index, the front wheel turning angle is calculated. With differential steering yaw moment The equilibrium solution between them; The state variables in the quadratic differential game model include: vehicle center of gravity sideslip angle. yaw rate Path tracking error, the control variable is the front wheel steering angle. With differential steering yaw moment The state-space equation is expressed as: ; In the formula, , These represent the lateral error and heading error between the vehicle and the reference trajectory, respectively. The longitudinal speed of the vehicle; For vehicle tire lateral stiffness; This refers to the vehicle's wheelbase. It is the moment of inertia; For the overall vehicle weight; The performance index function is defined as follows: ,in For state vectors, For control vectors, The state weight matrix is... To control the weight matrix, the Nash equilibrium between the front wheel steering angle and the differential steering yaw moment is solved by minimizing the performance index function.

[0009] Furthermore, step S3 specifically includes: Construct a phase plane with "center of mass sideslip angle" as the abscissa and "center of mass sideslip angular velocity" as the ordinate, and divide the phase plane into a stable region, an asymptotically stable region, and an unstable region; By combining the vehicle's current state parameters with the phase plane, the vehicle's stable state is identified in real time, including: stable state, asymptotically stable state, and unstable state. Specifically: when both the center of gravity sideslip angle and the center of gravity sideslip angular velocity are within the preset safety threshold range and the trajectory converges, it is determined to be a stable state; when the center of gravity sideslip angle or the center of gravity sideslip angular velocity exceeds the safety threshold but is still within the controllable convergence range, it is determined to be an asymptotically stable state; when both the center of gravity sideslip angle and the center of gravity sideslip angular velocity exceed the safety threshold and the trajectory diverges, it is determined to be an unstable state.

[0010] Furthermore, step S4 specifically includes: Fuzzy rules are used to assign weights to the differential steering yaw moment aimed at increasing agility and the stability control yaw moment aimed at increasing stability; where: When the vehicle is stable, retain the differential steering yaw moment obtained in step S2; When the system is in a state of asymptotic stability between stability and instability, the weights of the differential steering yaw moment and the stability control yaw moment are coordinated through fuzzy reasoning, and the yaw moment is obtained based on the weighted calculation of the two. When in an unstable state, only the stability control yaw moment is retained, and the differential steering yaw moment is completely eliminated.

[0011] Furthermore, in step S4, when the system is asymptotically stable between stability and instability, fuzzy reasoning is used to coordinate the weights of the differential steering yaw moment and the stability control yaw moment, and the yaw moment is obtained based on the weighted calculation of the two. Specific steps include: (1) Calculate the stability control yaw moment based on the current state deviation of the vehicle. ; (2) Assigning coordination weights through fuzzy reasoning Determine the differential steering yaw moment With stability control yaw moment The weighting ratio is as follows: the higher the stability requirement, the greater the weight of the stability control yaw moment; the higher the flexibility requirement, the greater the weight of the differential steering moment. (3) According to the formula The yaw moment is calculated to obtain the "synthetic yaw moment" that takes into account both requirements, so as to maintain tracking accuracy without loss of stability and prevent excessive tracking error.

[0012] Furthermore, in step S4, the stability control yaw moment is calculated. Specific methods include: The stability control yaw moment was obtained using the LQR algorithm. The inputs are the current yaw rate and the error between the center of mass sideslip angle and their ideal values; the output is the stability control yaw torque. ; The gain matrix of the Riccati equation is calculated iteratively. Finally obtained ,in The sideslip angle is the angle of the centroid. ω represents the yaw rate.

[0013] Furthermore, in step S3, the inputs to the fuzzy inference are the vehicle's longitudinal speed and the road surface adhesion coefficient, and the output is the coordination weights. ; The value range is [0,1]. When the vehicle's mobility requirement is higher than its stability requirement, ... The value is relatively large; when the vehicle stability requirement is higher than the mobility requirement. The value is relatively small.

[0014] Furthermore, the method also includes the following steps: The final yaw moment is distributed to the steering mechanism and the torque is sent to the wheel hub motor, respectively. Once the control flow ends, return to step S1 and continue executing in a loop.

[0015] A front-wheel-differential cooperative steering control system for a multi-axis distributed drive autonomous vehicle applying the above method mainly includes: The autonomous driving path planning module is used to generate the desired path based on environmental perception information. The mobility decision module is used to calculate the front wheel steering angle and differential steering yaw moment during the path tracking process of unmanned vehicles, with the goal of reducing the turning radius and increasing flexibility, using a quadratic differential game algorithm based on Nash equilibrium theory. The stability identification module is used to identify the stability of the vehicle based on the phase plane of the center of gravity sideslip angle. The cooperative steering control module is used for a weighted control strategy based on fuzzy rules to coordinate and distribute the differential steering yaw moment and the stability control yaw moment according to the current stability of the vehicle.

[0016] The method disclosed herein uses a quadratic differential game strategy based on Nash equilibrium theory to coordinate the front wheel steering angle and differential steering yaw moment of a multi-axis distributed drive unmanned vehicle, which can solve the problem of reduced path tracking performance caused by control conflicts between the two inputs. A weighted control strategy based on fuzzy rules is adopted to coordinate the allocation of differential steering yaw moment and stability control yaw moment. Specifically, fuzzy inference is used to calculate the coordination weights that are adapted to the current stable state of the vehicle, and the proportion of the two yaw moments is dynamically adjusted to achieve synchronous optimization of path tracking performance and driving stability under different stability levels.

[0017] Compared with the prior art, the beneficial effects of this disclosure are: (1) Coordinated control of front wheel steering angle and differential steering yaw moment The quadratic differential game strategy based on Nash equilibrium treats the front wheel steering angle and differential steering yaw moment of a multi-axis distributed drive unmanned vehicle as two game participants. By constructing a quadratic performance index that includes path tracking error and control cost, the two control inputs form a Nash equilibrium solution that is "mutually optimal response". This avoids interference and conflict caused by controlling the two inputs individually from the source. It retains the advantages of both inputs in improving maneuverability and maintains the vehicle's dynamic balance through cooperative adaptation. This can solve the problem of reduced path tracking performance caused by conflict. (2) Differential steering yaw moment - stability control yaw moment coordination control The weighted control strategy based on fuzzy rules takes the current stable state of the vehicle as the basis and generates coordinated weights that are adapted to the current stable state of the vehicle through fuzzy inference. It dynamically adjusts the ratio of differential steering yaw moment to stability control yaw moment. This on-demand dynamic allocation mechanism achieves a precise balance between the two objectives from the source of control, and ultimately achieves synchronous optimization of path tracking performance and driving stability under different stability levels. (3) Under stable operating conditions, the focus is on increasing the proportion of differential steering torque so that the vehicle can drive with higher flexibility, thereby enhancing the path tracking performance and tracking accuracy. (4) Increase the weight of stability torque under critical or unstable conditions, prioritize vehicle driving safety, and quickly suppress instability trend by adjusting wheel drive / braking force; (5) It can be applied to the intelligent control of various modes of transportation. Attached Figure Description

[0018] The above and other objects, features and advantages of this disclosure will become more apparent from the more detailed description of exemplary embodiments of this disclosure taken in conjunction with the accompanying drawings, in which the same reference numerals generally represent the same components.

[0019] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present disclosure. Detailed Implementation

[0020] Preferred embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0021] This disclosure provides a front-wheel-differential cooperative steering method and system for a multi-axis distributed drive autonomous vehicle. In one exemplary embodiment, the overall process is as follows: Figure 1 As shown, the main steps include: 1. Step 1: Obtain vehicle status and desired route S01, Obtain vehicle status and desired path. This step is the foundation of the control flow, collecting vehicle dynamics status (center of gravity sideslip angle, yaw rate, vehicle speed, etc.) and road adhesion coefficient information, while receiving the desired path generated by the autonomous driving path planning module.

[0022] 2. Step 2: Based on the quadratic differential game algorithm of Nash equilibrium theory, calculate the optimal front wheel steering angle and differential steering yaw moment during the path tracking process of the unmanned vehicle with the goal of reducing the turning radius and increasing flexibility.

[0023] S02, determine the initial performance indicators. With the initial goal of "improving flexibility + ensuring path tracking accuracy", output the vehicle's front wheel steering angle and differential steering yaw moment.

[0024] S03 utilizes a quadratic differential game strategy based on Nash equilibrium theory, and calculates the front wheel steering angle using path tracking accuracy as the performance indicator. With differential steering yaw moment The equilibrium solution between them.

[0025] 3. Step 3: Identify vehicle stability based on the phase plane of the centroid sideslip angle. S04, Construct the phase plane with the center of mass sideslip angle. This step is the foundation of stability identification. The stability identification module constructs a phase plane with "center of mass sideslip angle" as the abscissa and "center of mass sideslip angular velocity" as the ordinate, and divides the phase plane into a stable region, an asymptotically stable region, and an unstable region.

[0026] S05, Identify the stability domain and determine the vehicle state. Combine the current vehicle state parameters with the phase plane to identify the vehicle's stable state in real time.

[0027] 4. Step 4: Adopt a weighted control strategy based on fuzzy rules to coordinate and distribute the differential steering yaw moment and the stability control yaw moment according to the current stability of the vehicle.

[0028] S06, Steady-state control: When the state is determined to be stable, step S09 is executed, and the optimal front wheel steering angle output by the mobility decision module is retained. With differential steering yaw moment This allows the vehicle to move with greater agility and ensures its path-following performance.

[0029] S07, Asymptotic steady-state control: First, calculate the "stability control yaw moment" based on the current deviation of the vehicle's state. ”; Next, step S10 is executed to allocate coordination weights using fuzzy rules. The weight ratio of "differential steering yaw moment (flexibility)" and "stability control yaw moment (stability)" is determined. The higher the stability requirement, the greater the weight of stability control yaw moment; the higher the flexibility requirement, the greater the weight of differential steering moment. Finally, perform step S11 according to the formula. The yaw moment is calculated to obtain the "synthetic yaw moment" that takes into account both requirements, so as to maintain tracking accuracy without loss of stability and prevent excessive tracking error.

[0030] S08, Instability Control: When an unstable state is determined, step S12 is executed, the weight is adjusted to retain only the "stability control yaw moment", the differential steering yaw moment is completely canceled, the vehicle driving safety is prioritized, and the instability trend is quickly suppressed by adjusting the wheel drive / braking force.

[0031] In addition, this embodiment also includes the following steps: In step S13, the final yaw moment is distributed, and the steering input and torque are sent to the steering mechanism and wheel hub motor, respectively. The control process ends and returns to step S01 for continuous loop execution.

[0032] Furthermore, the aforementioned path tracking and stability coordinated control method also has the following additional technical features: In S01, the acquired vehicle status includes, but is not limited to: vehicle speed, wheel speed, center of gravity sideslip angle, center of gravity sideslip angular velocity, yaw rate, road surface adhesion coefficient, vehicle longitudinal acceleration, and lateral acceleration. The desired path is generated by the autonomous driving path planning module based on environmental perception information and includes parameters such as path coordinates, curvature, and rate of change of curvature.

[0033] In S02, the state variables of the quadratic differential game model include: vehicle center of gravity sideslip angle, yaw rate, and path tracking error. The control variables are the front wheel steering angle and differential steering yaw moment. The state-space equation is expressed as: The performance index function is defined as follows: ,in For state vectors, For control vectors, The state weight matrix is... To control the weight matrix, the Nash equilibrium between the front wheel steering angle and the differential steering yaw moment is solved by minimizing the performance index function.

[0034] In S04, the vehicle state types include: stable state, asymptotically stable state, and unstable state. Specifically: when both the sideslip angle and the sideslip velocity are within the preset safety threshold range and the trajectory converges, it is determined to be a stable state; when the sideslip angle or the sideslip velocity exceeds the safety threshold but is still within the controllable convergence range, it is determined to be an asymptotically stable state; when both the sideslip angle and the sideslip velocity exceed the safety threshold and the trajectory diverges, it is determined to be an unstable state.

[0035] In S07, stability control yaw moment The LQR algorithm can be used to determine the yaw rate, the error between the current yaw rate and the center of mass sideslip angle and their ideal values, and the stability control yaw moment. The gain matrix of the Riccati equation is calculated iteratively. Finally obtained ,in The sideslip angle is the angle of the centroid. ω represents the yaw rate.

[0036] In step S07, the inputs to the fuzzy inference are the vehicle's longitudinal speed and the road surface adhesion coefficient, and the output is the coordination weights. , The value range is [0,1]. When the vehicle's mobility requirement is higher than its stability requirement, ... The value is relatively large; when the vehicle stability requirement is higher than the mobility requirement. The value is relatively small.

[0037] In this embodiment, the front wheel steering angle and differential steering yaw moment of the multi-axis distributed drive unmanned vehicle are controlled in a coordinated manner by using a quadratic differential game strategy based on Nash equilibrium theory, which can solve the problem of reduced path tracking performance caused by control conflict between the two inputs. A weighted control strategy based on fuzzy rules is adopted to coordinate the allocation of differential steering yaw moment and stability control yaw moment. Specifically, fuzzy inference is used to calculate the coordination weights that are adapted to the current stable state of the vehicle, and the proportion of the two yaw moments is dynamically adjusted to achieve synchronous optimization of path tracking performance and driving stability under different stability levels.

[0038] Application scenario examples Scenario 1: Dry road narrow road turning (stable state). First, the front wheel steering angle and differential steering yaw moment acting on the path tracking are calculated through a quadratic differential game strategy. Then, based on the vehicle's center of gravity sideslip angle and sideslip angular velocity, and according to the phase plane, the vehicle is determined to be in a stable state. Finally, the execution output module sends the command to the steering actuator and drive motor, and the vehicle passes through the narrow road curve with a small turning radius and no tendency to lose stability.

[0039] Scenario 2: Continuous curves on a semi-dry road after rain (asymptotically stable state). A quadratic differential game strategy is used to calculate the front wheel steering angle and differential steering yaw moment acting on path tracking; the stability identification module determines the vehicle is in an "asymptotically stable state" through the phase plane. In the third step, by adjusting the fuzzy inference logic, the coordination weights are allocated to "differential steering yaw moment and stability-added yaw moment each accounting for a certain proportion," retaining some flexibility to cope with continuous curves while suppressing state deterioration through the stability moment; when the vehicle passes through continuous curves, it neither loses path tracking accuracy nor exhibits instability.

[0040] Scenario 3: Sudden sideslip on a wet road surface (instability). First, a quadratic differential game strategy is used to determine the front wheel steering angle and differential steering yaw moment. Second, the stability identification module determines that the vehicle is in an unstable state and sends the result to the execution module. Third, the execution module cancels the differential steering yaw moment, generates a stability-additional yaw moment, and the execution output module controls the drive motor to generate a stabilizing torque, quickly suppressing the vehicle's sideslip tendency and returning it to a stable driving trajectory.

[0041] The above technical solutions are merely exemplary embodiments of the present invention. For those skilled in the art, based on the application methods and principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the methods described in the specific embodiments of the present invention. Therefore, the methods described above are merely preferred and not restrictive.

Claims

1. A front-wheel-differential cooperative steering control method for a multi-axis distributed drive unmanned vehicle, characterized in that, Includes the following steps: S1, obtain vehicle status and desired path; S2, a quadratic differential game algorithm based on Nash equilibrium theory, calculates the optimal front wheel steering angle and differential steering yaw moment during the path tracking process of an autonomous vehicle with the goal of reducing the turning radius and increasing flexibility. S3 identifies vehicle stability based on the phase plane of the centroid sideslip angle; S4 employs a weighted control strategy based on fuzzy rules to coordinate and distribute the differential steering yaw moment and the stability control yaw moment according to the vehicle's current stability.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: Acquire vehicle dynamics and road surface adhesion coefficient information; simultaneously receive the desired path generated by the autonomous driving path planning module; The vehicle dynamics states include: vehicle speed, wheel speed, sideslip angle, sideslip velocity, yaw rate, road adhesion coefficient, longitudinal acceleration, and lateral acceleration. The desired path is generated by the autonomous driving path planning module based on environmental perception information and includes path coordinates, curvature, and curvature change rate parameters.

3. The method according to claim 1, characterized in that, Step S2 specifically includes: Determine initial performance indicators: With the goal of "improving flexibility + ensuring path tracking accuracy", output the vehicle's front wheel steering angle and differential steering yaw moment; Using a quadratic differential game strategy based on Nash equilibrium theory, and taking path tracking accuracy as the performance index, the front wheel turning angle is calculated. With differential steering yaw moment The equilibrium solution between them; The state variables in the quadratic differential game model include: vehicle center of gravity sideslip angle. yaw rate Path tracking error, the control variable is the front wheel steering angle. With differential steering yaw moment The state-space equation is expressed as: ； In the formula, , These represent the lateral error and heading error between the vehicle and the reference trajectory, respectively. The longitudinal speed of the vehicle; For vehicle tire lateral stiffness; This refers to the vehicle's wheelbase. It is the moment of inertia; For the overall vehicle weight; The performance index function is defined as follows: ,in For state vectors, For control vectors, The state weight matrix is... To control the weight matrix, the Nash equilibrium between the front wheel steering angle and the differential steering yaw moment is solved by minimizing the performance index function.

4. The method according to claim 1, characterized in that, Step S3 specifically includes: Construct a phase plane with "center of mass sideslip angle" as the abscissa and "center of mass sideslip angular velocity" as the ordinate, and divide the phase plane into a stable region, an asymptotically stable region, and an unstable region; By combining the vehicle's current state parameters with the phase plane, the vehicle's stable state is identified in real time, including: stable state, asymptotically stable state, and unstable state. Specifically: when both the center of gravity sideslip angle and the center of gravity sideslip angular velocity are within the preset safety threshold range and the trajectory converges, it is determined to be a stable state; when the center of gravity sideslip angle or the center of gravity sideslip angular velocity exceeds the safety threshold but is still within the controllable convergence range, it is determined to be an asymptotically stable state; when both the center of gravity sideslip angle and the center of gravity sideslip angular velocity exceed the safety threshold and the trajectory diverges, it is determined to be an unstable state.

5. The method according to claim 1, characterized in that, Step S4 specifically includes: Fuzzy rules are used to assign weights to the differential steering yaw moment aimed at increasing agility and the stability control yaw moment aimed at increasing stability; where: When the vehicle is stable, retain the differential steering yaw moment obtained in step S2; When the system is in a state of asymptotic stability between stability and instability, the weights of the differential steering yaw moment and the stability control yaw moment are coordinated through fuzzy reasoning, and the yaw moment is obtained based on the weighted calculation of the two. When in an unstable state, only the stability control yaw moment is retained, and the differential steering yaw moment is completely eliminated.

6. The method according to claim 5, characterized in that, In step S4, when the system is asymptotically stable between stability and instability, fuzzy reasoning is used to coordinate the weights of the differential steering yaw moment and the stability control yaw moment, and the yaw moment is obtained based on the weighted calculation of the two. The specific steps include: (1) Calculate the stability control yaw moment based on the current state deviation of the vehicle. ; (2) Assigning coordination weights through fuzzy reasoning Determine the differential steering yaw moment With stability control yaw moment The weighting ratio is as follows: the higher the stability requirement, the greater the weight of the stability control yaw moment; the higher the flexibility requirement, the greater the weight of the differential steering moment. (3) According to the formula The yaw moment is calculated to obtain the "synthetic yaw moment" that takes into account both requirements, so as to maintain tracking accuracy without loss of stability and prevent excessive tracking error.

7. The method according to claim 6, characterized in that, In step S4, the stability control yaw moment is calculated. Specific methods include: The stability control yaw moment was obtained using the LQR algorithm. The inputs are the current yaw rate and the error between the center of mass sideslip angle and their ideal values; the output is the stability control yaw torque. ; The gain matrix of the Riccati equation is calculated iteratively. Finally obtained ,in The sideslip angle is the angle of the centroid. ω represents the yaw rate.

8. The method according to claim 6, characterized in that, In step S3, the inputs to the fuzzy inference are the vehicle's longitudinal speed and the road surface adhesion coefficient, and the output is the coordination weights. ; The value range is [0,1]. When the vehicle's mobility requirement is higher than its stability requirement, ... The value is relatively large; when the vehicle stability requirement is higher than the mobility requirement. The value is relatively small.

9. The method according to any one of claims 1-8, characterized in that, It also includes the following steps: The final yaw moment is distributed to the steering mechanism and the torque is sent to the wheel hub motor, respectively. Once the control flow ends, return to step S1 and continue executing in a loop.

10. A front-wheel-differential cooperative steering control system for a multi-axis distributed drive unmanned vehicle applying the method of any one of claims 1-9, characterized in that, include: The autonomous driving path planning module is used to generate the desired path based on environmental perception information. The mobility decision module is used to calculate the front wheel steering angle and differential steering yaw moment during the path tracking process of unmanned vehicles, with the goal of reducing the turning radius and increasing flexibility, using a quadratic differential game algorithm based on Nash equilibrium theory. The stability identification module is used to identify the stability of the vehicle based on the phase plane of the center of gravity sideslip angle. The cooperative steering control module is used for a weighted control strategy based on fuzzy rules to coordinate and distribute the differential steering yaw moment and the stability control yaw moment according to the current stability of the vehicle.

Images