Distributed driving automobile path tracking and stability control optimization method and related equipment

Abstract

The invention belongs to the field of distributed driving automobile control, and discloses a distributed driving automobile path tracking and stability control optimization method and related equipment, and the method comprises the steps: firstly building a tire cornering stiffness dynamic range through multi-source sensing data, and reducing the influence of nonlinear characteristics on a model; then, through a dynamic output feedback model prediction control algorithm, an estimation state sequence and an error boundary of a vehicle side slip angle are calculated online, an optimal control gain is matched in real time based on a two-dimensional table, a combined control quantity of a four-wheel steering angle and an additional yawing moment is formed, and parameter uncertainty caused by vehicle speed change is effectively compensated; and finally, torque distribution is carried out by adopting a quadratic programming algorithm and taking tire utilization rate minimization as a target. According to the method, the vehicle path tracking precision and the transverse stability under the complex working condition are improved, meanwhile, the tire saturation risk is reduced, the tire utilization rate is reduced, the transverse tracking error is reduced, and unified optimization of safety and controllability in the automatic driving process is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of distributed drive vehicle control, and particularly relates to an optimization method for path tracking and stability control of a distributed drive vehicle and related devices. Background Art

[0002] At present, with the rapid development of intelligent transportation systems, autonomous vehicles have become the focus of research in academia and industry due to their intelligence and safety. Among them, electric vehicles driven by in-wheel motors have significant advantages in that they are equipped with advanced four-wheel steer-by-wire motors and drive motors, and have the remarkable advantages of adjustable four-wheel steering angles and controllable four-wheel torques. This characteristic enables it to develop a variety of advanced driver assistance systems, such as active four-wheel steering systems, direct yaw moment control systems, and integrated control systems, etc., thereby effectively improving the driving safety of vehicles and being regarded as an important direction for the development of future autonomous vehicles.

[0003] Currently, in order to achieve accurate tracking of the desired path by autonomous vehicles and enhance active safety, coordinated control systems of active rear-wheel steering and direct yaw moment systems, and integrated control systems of four-wheel steering systems and direct yaw moment have been successively established. However, due to the non-linear characteristics of tires and the dynamic changes in longitudinal vehicle speed, in the practical application of the combined system of 4WS (four-wheel steering system) and DYC (direct yaw moment control system), the problem of vehicle parameter uncertainty becomes prominent. Existing technologies have obvious deficiencies in dealing with this challenge and have not yet formed effective solutions.

[0004] Therefore, how to overcome the influence brought by the non-linear characteristics of tires and the change in longitudinal vehicle speed, and solve the problem of vehicle parameter uncertainty in the combined system of four-wheel steering system and direct yaw moment control system, so as to further improve the active safety performance of in-wheel motor driven electric vehicle autonomous driving, has become an urgent technical problem to be solved. Summary of the Invention

[0005] The present invention provides an optimization method for path tracking and stability control of a distributed drive vehicle and related devices. By using this method, the problem of vehicle parameter uncertainty in the combined system of four-wheel steering system and direct yaw moment control system can be effectively solved, and the active safety performance of in-wheel motor driven electric vehicle autonomous driving is improved.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: An optimization method for path tracking and stability control of a distributed drive vehicle, comprising: Identifying the road surface type based on visual information and lidar point cloud data, and estimating the tire cornering stiffness range based on the road surface type; Based on the pre-estimated vehicle sideslip angle at the center of mass, combined with the dynamic output feedback model predictive control algorithm, the controller gain coefficients and the observation error weights corresponding to the estimated state sequences and the estimated error boundary sequences at different times are obtained; the optimal control gain is obtained through querying a two-dimensional table model, and the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequences, the estimated error boundary sequences and the control gains; based on the optimal control gain and the tire cornering stiffness range, the optimal control quantities are calculated, and the optimal control quantities include the four-wheel steering angles and the additional yaw moment. Based on the four-wheel steering angles and the additional yaw moment, with the goal of minimizing the tire utilization rate, the driving torque is optimized and distributed by using the quadratic programming algorithm to obtain the optimal four-wheel driving torque, so as to realize the collaborative optimization of the path tracking and the stability control of the distributed drive vehicle.

[0007] Further, the identification of the road surface type based on the visual information and the lidar point cloud data includes: The YOLOv8 network model is used to extract features from the visual information, and the extracted features are input into the trained classifier to output the first road surface type recognition result and the corresponding first confidence level; The lidar point cloud data is input into the trained BP neural network to output the second road surface type recognition result and the corresponding second confidence level; the specific formula for calculating the confidence level is as follows:

[0008] In the formula, is the confidence level of the output result, is the standard deviation, is the neural network input quantity, is the neural network output result, is the road surface type label value, is a constant; Based on the first road surface type recognition result, the first confidence level, the second road surface type recognition result, and the second confidence level, the fused confidence level and the road surface type corresponding to the fused confidence level are calculated, and the specific formula is as follows:

[0009]

[0010]

[0011] In the formula, is the first road surface type recognition result, is the first confidence level; is the second road surface type recognition result, is the second confidence level; It is the road surface type of the final output, which is equal to the road surface type corresponding to the fusion confidence level; It is the fusion confidence level; It is the excitation factor; It is the penalty factor.

[0012] Furthermore, the range of tire cornering stiffness estimated based on the road surface type includes: Obtain the road surface adhesion coefficient based on the road surface type; Input the road surface adhesion coefficient, tire cornering angle, and wheel vertical force into a composite tire model combining the brush tire model and the magic formula to output the range of tire cornering stiffness; among them, the basic model of the composite tire model uses a BP neural network; The specific expression of the composite tire model is as follows:

[0013] In the formula, is the tire lateral force, is the cornering stiffness, is the road surface adhesion coefficient, is the tire cornering angle, is the tangent function, sin is the sine function, arctan is the arctangent function, is the absolute value of, are different fitting coefficients of the magic tire respectively, , representing the fitting stiffness coefficient, is the wheel vertical force, is the tire cornering angle threshold, is the correction parameter.

[0014] Furthermore, based on the pre-estimated vehicle centroid side slip angle, combined with the dynamic output feedback model predictive control algorithm, the controller gain coefficients and observation error weights corresponding to the estimated state sequences and estimated error boundary sequences at different times are obtained; the optimal control gain is obtained by querying through a two-dimensional table model, including: Based on the pre-estimated vehicle centroid side slip angle, combined with the dynamic output feedback model predictive control algorithm, calculate the control gains and observation error weights corresponding to the estimated state sequences and estimated error boundary sequences at different times; Construct a two-dimensional table model based on the estimated state sequences and estimated error boundary sequences; According to the current estimated state and estimated error boundary, query the two-dimensional table model to obtain the controller gain coefficients and observation error weights; Calculate the optimal control gain according to the queried controller gain coefficients and observation error weights.

[0015] Further, the optimal control quantity is calculated based on the optimal control gain and the tire cornering stiffness range, and the optimal control quantity includes the four-wheel steering angles and the additional yaw moment, including: According to the tire cornering stiffness range, the vertices of the pre-constructed LPV model are dynamically adjusted to reduce the uncertain boundary of the LPV model, and the scheduling parameters for solving the optimal control quantity are calculated; the LPV model is used to reflect the dynamic motion state of the vehicle under the current road scenario; Based on the optimal control gain, the current estimated state variables, and the scheduling parameters, the optimal control quantity is calculated. The specific formula is as follows:

[0016] In the formula, is the optimal control quantity; is the scheduling parameter; is the current estimated state variable; is the optimal control gain.

[0017] Further, the specific expression of the LPV model is as follows:

[0018] In the formula, is the maximum vehicle speed, is the minimum vehicle speed, is the maximum cornering stiffness of the front axle, is the minimum cornering stiffness of the front axle, is the maximum cornering stiffness of the rear axle, is the minimum cornering stiffness of the rear axle; respectively represent different reference variables, where j takes the value of 1 or 2.

[0019] Further, based on the four-wheel steering angles and the additional yaw moment, with the minimum tire utilization rate as the goal, the driving torque is optimized and distributed by using the quadratic programming algorithm to obtain the optimal four-wheel driving torques, including: Taking the minimum tire utilization rate as the optimization goal J, the driving torque is optimized and distributed based on the quadratic programming algorithm, and the optimal four-wheel driving torques are calculated. The specific expression is as follows:

[0020] In the formula, are the longitudinal tire force and the vertical tire force respectively; is the road surface adhesion coefficient; Constraint conditions:

[0021] In the formula, The tire driving forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively, is the front wheel angle of the four-wheel steering angle, is the wheelbase of the vehicle in front, is the wheel radius, is the additional yaw moment, is the peak torque of the in-wheel motor, is the total longitudinal driving torque.

[0022] An optimization system for path tracking and stability control of a distributed drive vehicle, comprising: An estimation module, configured to identify the road surface type based on visual information and lidar point cloud data, and estimate the tire cornering stiffness range based on the road surface type; A first calculation module, configured to, based on the pre-estimated vehicle center of mass sideslip angle, combine the dynamic output feedback model predictive control algorithm to obtain the controller gain coefficients and observation error weights corresponding to the estimated state sequence and the estimated error boundary sequence at different times; query the optimal control gain through a two-dimensional table model, where the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence, and the control gain; calculate the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, and the optimal control quantity includes the four-wheel steering angle and the additional yaw moment; A second calculation module, configured to, based on the four-wheel steering angle and the additional yaw moment, with the goal of minimizing the tire utilization rate, optimize the distribution of the driving torque using the quadratic programming algorithm to obtain the optimal four-wheel driving torque, so as to achieve the collaborative optimization of path tracking and stability control of the distributed drive vehicle.

[0023] An electronic device, comprising: A memory, configured to store a computer program; A processor, configured to implement the steps of the above-mentioned path tracking and stability control optimization method for a distributed drive vehicle when executing the computer program.

[0024] A computer-readable storage medium, the computer-readable storage medium stores a computer program, and the computer program is used to implement the steps of the above-mentioned path tracking and stability control optimization method for a distributed drive vehicle when executed by a processor.

[0025] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an optimization method for path tracking and stability control of a distributed-drive vehicle. First, a dynamic range of tire cornering stiffness is constructed using multi-source perception data to reduce the influence of non-linear characteristics on the model. Subsequently, through a dynamic output feedback model predictive control algorithm, the estimated state sequence and error boundary of the vehicle's center-of-mass sideslip angle are calculated online, and the optimal control gain is matched in real time based on a two-dimensional table to form a combined control quantity of four-wheel steering angles and additional yaw moments, effectively compensating for parameter uncertainties caused by vehicle speed changes. Finally, a quadratic programming algorithm is used to perform torque distribution with the goal of minimizing tire utilization, ensuring the optimal distribution of four-wheel driving torques under the friction circle constraint. Through three-layer mechanisms of online parameter estimation, dynamic adjustment of robust control gain, and torque optimization distribution, this method significantly improves the vehicle's path tracking accuracy and lateral stability under complex conditions, while reducing the risk of tire saturation, lowering tire utilization, reducing lateral tracking error, and achieving unified optimization of safety and controllability during the autonomous driving process. In the present invention, preferably, the final road surface type is output by fusing the YOLOv8 visual feature extraction and the classification results of the BP neural network of lidar point clouds, and combining a confidence-weighted fusion strategy. By taking advantage of the complementary strengths of visual information in capturing texture features and lidar in perceiving geometric features, excitation factors and penalty factors are introduced through a confidence formula to strengthen the weights of high-confidence data. This improves the accuracy and robustness of road surface type recognition in complex environments, avoids parameter estimation biases caused by misjudgment of a single sensor, and provides a reliable input for subsequent tire stiffness estimation. In the present invention, preferably, a composite tire model (combining the brush model and the magic formula) is adopted and the tire lateral force characteristics are fitted based on a BP neural network. The brush model describes the mechanical characteristics of the linear region, and the magic formula corrects the non-linear saturation characteristics. Parameters such as road surface adhesion coefficient and vertical force are dynamically correlated through the neural network. This realizes the dynamic estimation of the range of tire cornering stiffness under different road surfaces and load conditions, solves the problem of insufficient adaptability of traditional single models in the non-linear region, and improves the characterization accuracy of the control algorithm for tire mechanical characteristics.

[0026] In the present invention, preferably, an estimated state sequence and error boundary are generated in real time based on the dynamic output feedback model predictive control algorithm, and the optimal control gain is matched through mapping of a two-dimensional table. The state estimation and error boundary are quantified into a discretized parameter space, and offline pre-computation and online table lookup are used to reduce the computational complexity. This realizes the dynamic adaptive adjustment of the controller gain, enhances the robustness of the system to parameter perturbations and observation noise while ensuring real-time performance, and solves the defect of insufficient ability of traditional fixed-gain control to cope with vehicle speed changes. In the present invention, preferably, by dynamically adjusting the vertex parameters and scheduling parameters of the LPV model, and combining with the optimal control gain, the four-wheel steering angle and the additional yaw moment are generated. The vertices of the LPV model are updated in real time based on the tire stiffness range to narrow the uncertainty boundary, and the scheduling parameters are used to correlate the vehicle speed change with the control quantity calculation. The adaptability of the vehicle dynamics model under different vehicle speeds and road conditions is enhanced, the model linearization error is reduced through parameter dynamic compensation, and the accuracy of the coordinated control of the four-wheel steering angle and the direct yaw moment is improved. In the present invention, preferably, an LPV model expression including the vehicle speed and the extreme values of the cornering stiffness is constructed to cover the dynamic characteristics of the vehicle under all working conditions. The vehicle speed and the extreme values of the tire stiffness are used as vertex parameters, and the parameter change range is covered by the polytope model, ensuring the completeness of the LPV model under different extreme working conditions, avoiding model mismatch caused by parameter out-of-bounds, and providing a stable and reliable vehicle dynamics description framework for the control algorithm. In the present invention, preferably, with the goal of minimizing the tire utilization rate, the friction circle constraint and the motor torque limit are introduced into the driving torque distribution through the quadratic programming algorithm. The coupling relationship between the longitudinal force and the lateral force of the tire is transformed into a quadratic programming problem, and the equality constraint is constructed in combination with the additional yaw moment requirement, realizing the global optimal distribution of the four-wheel driving torque, maximizing the utilization of the tire friction potential while meeting the path tracking requirement, and avoiding the risk of excessive slip of a single wheel or motor overload. Description of the Drawings Figure 1 is the execution flowchart of the distributed drive vehicle path tracking and stability control optimization system provided by the embodiment of the present invention; Figure 2 is the structural block diagram of the road surface type fusion estimator provided by the embodiment of the present invention; Figure 3 is the bounded range of the tire cornering stiffness provided by the embodiment of the present invention; Figure 4 is the flowchart of a distributed drive vehicle path tracking and stability control optimization method provided by the embodiment of the present invention; Figure 5 is the structural schematic diagram of a distributed drive vehicle path tracking and stability control optimization system provided by the embodiment of the present invention. Detailed Embodiments

[0027] This embodiment provides a distributed drive vehicle path tracking and stability control optimization method, which is characterized by including: Identifying the road surface type based on the visual information and the lidar point cloud data, and estimating the tire cornering stiffness range based on the road surface type; Based on the pre - estimated vehicle sideslip angle of the center of mass, combined with the dynamic output feedback model predictive control algorithm, the controller gain coefficients and the observation error weights corresponding to the estimated state sequences and the estimated error boundary sequences at different times are obtained; the optimal control gain is obtained through querying the two - dimensional table model, and the two - dimensional table model is used to reflect the mapping relationship between the estimated state sequences, the estimated error boundary sequences and the control gain; based on the optimal control gain and the tire cornering stiffness range, the optimal control quantities are calculated, and the optimal control quantities include the four - wheel steering angles and the additional yaw moment. Based on the four - wheel steering angles and the additional yaw moment, with the goal of minimizing the tire utilization rate, the drive torque is optimized and distributed by using the quadratic programming algorithm to obtain the optimal four - wheel drive torque, so as to realize the collaborative optimization of the path tracking and stability control of the distributed - drive vehicle.

[0028] The optimization method provided in this embodiment will be further described below with reference to the accompanying drawings: As Figure 1 shown, this embodiment provides a distributed - drive vehicle path - tracking and stability - control optimization system for implementing the above - mentioned distributed - drive vehicle path - tracking and stability - control optimization steps, including an estimation layer, a control layer and a distribution layer. The specific implementation steps are as follows: S1: Applied to the estimation layer, based on the vision and lidar fusion estimation to identify the road surface type, and the road surface type is transmitted to the tire cornering stiffness estimator to obtain the real - time tire cornering stiffness range; the tire cornering stiffness range is used to continuously shrink the vertices of the uncertain LPV model and reduce the model uncertainty boundary; and a sideslip angle observer of the center of mass is designed to estimate the sideslip angle of the center of mass.

[0029] S2: Applied to the control layer, an RMPC controller based on the sideslip angle observer of the center of mass is constructed to obtain the control law. At the same time, a two - dimensional table model about the estimated state sequences and the estimated error boundary sequences is constructed to query the optimal control gain, and then the four - wheel steering angles and the additional yaw moment are determined according to the optimal control gain.

[0030] S3: Applied to the distribution layer, according to the four - wheel steering angles and the additional yaw moment, with the goal of minimizing the tire load rate, the drive torque determined in S2 is optimized and distributed based on the quadratic programming algorithm.

[0031] Exemplarily, the specific steps of S1 are as follows: A road - type estimator based on vision and lidar fusion is established. As Figure 2 shown, it mainly includes a data recognition module, a spatio - temporal synchronization module, a reliability detection module and a decision - making fusion module.

[0032] In the data recognition module, the YOLOv8 network model and the BP neural network (BPNN) are respectively used for road type estimation of visual information and lidar point cloud information; to maintain the spatio-temporal synchronization of the lidar and the camera, the extracted lidar point cloud data needs to be consistent with the image preview position; the reliability detection module is mainly composed of a confidence threshold monitoring unit and an illuminance detection unit.

[0033] First, the YOLOv8 network model and the BP neural network (BPNN) are respectively used by the visual information and lidar modules for road type estimation. The road type output by the visual information module is , and the confidence level is ; the road type output by the lidar module is , and the confidence level is . The confidence level is specifically defined as follows: (1) In the formula, is the confidence level of the output result, is the standard deviation, is the output result of the neural network, is the output result of the neural network, is the pavement type label value, is a constant.

[0034] After that, the spatio-temporal synchronization of the lidar and camera information is performed. In this embodiment, the preview position of the camera is set to the center position of the road area, and the extracted lidar point cloud data needs to be consistent with the image preview position. In addition, assuming that the acquisition frequencies of the lidar and the camera are 10hz and 30hz respectively, in order to maintain temporal consistency, the lidar acquires data every 1 frame, and the visual sensor acquires data every 3 frames, and the sampling period is 100ms.

[0035] In addition, since the recognition accuracy of the visual algorithm is affected by the illumination intensity, it is necessary to detect the illuminance of the picture information. Based on the Retinex model, it is assumed that the three channels share the same illumination channel. Since the reliability of the picture information is affected by the illumination intensity and distribution, the illumination intensity is the algebraic sum of the maximum RGB values of each pixel point of the image, and the illumination distribution is the variance of the maximum RGB value of the image pixel points. Only when both the algebraic sum and the variance are within the appropriate range, the visual information is considered to have passed the illuminance detection. At the same time, set the confidence threshold , and detect whether the confidence levels of the visual and lidar detections meet the conditions & .

[0036] Finally, decision fusion is performed on the data, and the finally output pavement type is , and the confidence level is When the output information of the two sensors is the same, that is , the output road surface type gives a positive incentive to the fusion confidence ; when , the output road surface type depends on the one with the higher confidence, and at the same time gives a negative penalty to the fusion confidence . The confidence is as follows: (2) In the formula, is the incentive factor, is the penalty factor, and max{} represents the maximum value of the two variables in {}.

[0037] A composite tire model that combines the comprehensive brush tire model and the magic formula is established. The composite tire model is used to estimate the tire cornering stiffness in the linear and nonlinear regions. The specific expression is as follows: (3) In the formula, is the tire lateral force, is the cornering stiffness, is the road surface adhesion coefficient, is the tire slip angle, is the tangent function, sin is the sine function, arctan is the arctangent function, is the absolute value of, is the magic tire fitting coefficient, represents the fitted stiffness coefficient, is the wheel vertical force, is the tire slip angle threshold, is the correction parameter.

[0038] In order to obtain the cornering stiffness and the cornering stiffness range in real time, combining the relationship between the cornering stiffness in formula (3) and the road surface adhesion coefficient, tire slip angle and wheel vertical force, a mapping relationship between the road surface adhesion coefficient, tire slip angle and wheel vertical force and the tire cornering stiffness is established using a BP neural network. The input parameters are the tire slip angle, normal force and road surface adhesion coefficient, and the output parameters are the cornering stiffness of the front and rear wheels.

[0039] According to the road surface type obtained by the sensor fusion estimator, the tire cornering stiffness range is dynamically adjusted. As Figure 3 shown, when the road surface type identification result is a concrete road surface, its adhesion coefficient range is , and under a fixed normal load, the corresponding tire cornering stiffness range within the slip angle threshold range is ; The actual tire cornering stiffness range is , which continuously shrinks as the sideslip angle increases. Based on this, the cornering stiffness range is continuously adjusted, thereby continuously shrinking the vertices of the uncertain LPV model and reducing the uncertain boundary of the model to more accurately reflect the dynamic motion state of the vehicle under the current road scenario. The LPV model system based on polytope is defined as follows: (4) where, is the maximum vehicle speed, is the minimum vehicle speed, is the maximum cornering stiffness of the front axle, is the minimum cornering stiffness of the front axle, is the maximum cornering stiffness of the rear axle, is the minimum cornering stiffness of the rear axle, is a different reference variable.

[0040] The LPV system uses a polyhedron with 2 4 = 16 vertices to cover all possible pairs of uncertain parameters selection, and 4 time-varying parameters can be obtained by summing the vertex coordinates, and the expression is as follows: (5) where, is the weighting factor, is the vehicle speed, is the cornering stiffness of the front axle, is the cornering stiffness of the rear axle.

[0041] Based on this, the LPV model considering uncertain parameters can be derived as follows: (6) where, is the state variable input, is the derivative value of the state variable input, , , , is the scheduling parameter, is the system matrix at the vertices of the polyhedron related to vehicle parameters, is the system perturbation.

[0042] For the uncertainties of vehicle system parameters, estimation error uncertainties, and bounded perturbations, an observer based on robust control is established to estimate the vehicle's center of mass sideslip angle, as shown in the following equation: (7) where, is the estimated state variable at time k, is the estimated state variable at time k+1, is the estimated value of the lateral error in path tracking, is the estimated value of the heading angle error in path tracking, is the estimated value of the sideslip angle at the center of mass, is the estimated value of the yaw rate error, is the estimated value of the roll angle error, is the estimated value of the roll rate error, is the control law at time k, is the actual measured output at time k, is the controller gain related to the parameters, is the constant matrix related to the dynamic model.

[0043] The system estimation error state equation is: (8) where, is the observation error at time k, is the observation error at time k+1, is the observer gain related to the parameters, is the system disturbance at time k, is the constant matrix related to the dynamic model.

[0044] The optimal observer gain can be obtained through the following optimal problem: There exists a symmetric positive definite matrix , such that the matrix satisfies the constraints of the following equations (9) and (10) (9) (10) where, the observed variable , is the robust invariant set related to the disturbance, is the constant matrix related to the dynamic model.

[0045] The final observer gain can be expressed as: (11) where, is the observer gain, Exemplarily, the specific steps of S2 are: Construct an RMPC controller based on the vehicle sideslip angle observer and a two-dimensional tabular model regarding the estimated state and the estimated error bound to determine the steering angle and torque to achieve path tracking, including: First, taking the vehicle sideslip angle as the input, based on the dynamic output feedback model predictive control algorithm (RMPC), a min-max optimization problem is established to minimize the infinite-horizon quadratic objective function of the vehicle under the worst case. The expression is as follows: (12) (13) (14) (15) (16) (17) In the formula, is the quadratic cost function, is the output at the (k + i)-th moment when the current moment is k, is the control law at the (k + i)-th moment when the current moment is k, is the estimated state variable at the (k + i + 1)-th moment when the current moment is k, is the constant matrix related to the dynamic model, indicates that these matrices are in the uncertainty set, is the symmetric positive definite weight matrix, and the matrices represent the weight matrices of the observation vector and the observation error respectively, represents the elliptical robust invariant set related to the matrix ; is the transpose of the estimated state variable and the observation error matrix at the k-th moment, represent the transposes of the estimated state variable and the observation error matrix at the (k + i)-th moment and the (k + i + 1)-th moment when the current moment is k respectively, is the performance index, are the constraints of the control input and the system state respectively, and the matrix is the state matrix constraint factor.

[0046] To ensure the stability of the closed-loop system, the performance objective function (12) must be bounded, so it is necessary to satisfy . Based on this, summing up the formula (15) from to gives: (18) In the formula, is the performance index at the k-th moment, is the transpose of the estimated state variable and the observation error matrix at the k-th moment when the current moment is k.

[0047] Obtained from formula (14) The optimization problem of minimizing the quadratic cost function in the infinite time domain can be transformed into the problem of minimizing the upper bound, and the specific expression is as follows: (19) wherein, is a scalar satisfying , is a symmetric matrix satisfying the following formula: (20) (21) wherein, , is the controller gain related to the parameters.

[0048] Secondly, the estimation error bound is updated online. By calculating the following inequality, the minimum bound of the estimation error set at time is obtained.

[0049] (22) (23) wherein, , , , is a scalar satisfying the following formula: (24) Finally, in order to improve the calculation speed of the model, in this embodiment, a two-dimensional offline table lookup model (two-dimensional table model) of the estimated state sequence and the estimated error bound sequence is pre-constructed, and the control law is searched according to the system real-time estimated state sequence and the estimated error bound, that is, the optimal control gain is found by lookup. It is specifically divided into the following two steps: The first step: Select a group of estimated state sequences , satisfying , and assuming , select a group of estimated error bound sequences , satisfying ; The second step: Substitute each combination into the following formula: (25) wherein, the scalar , is the estimation error bound, is the identity matrix.

[0050] By solving the minimization-maximization formula (19), the minimum value can be calculated. , . Furthermore, the optimal control gain can be obtained.

[0051] (27) In the formula, is the controller gain coefficient corresponding to the state estimate t and the estimation error boundary r at time k, is the observation error weight corresponding to the state estimate t and the estimation error boundary r at time k.

[0052] Therefore, the final control quantity can be expressed as: (28) Exemplarily, the specific steps of S3 are as follows: Optimally allocate the driving torque based on the quadratic programming algorithm, specifically including: Taking the minimum tire utilization rate as the objective function, the expression is as follows: (29) In the formula, are the longitudinal and vertical tire forces respectively.

[0053] Constraint conditions: (30) In the formula, are the tire driving forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively. The four-wheel steering angles include the front-wheel steering angle and the rear-wheel steering angle. Among them, is the front-wheel steering angle of the four-wheel steering angle, is the wheelbase of the front vehicle, is the wheel radius, is the additional yaw moment, is the peak torque of the in-wheel motor, is the total longitudinal driving torque obtained through the speed tracking PID controller.

[0054] Finally, use quadratic programming to solve equation (29) to calculate the optimal four-wheel driving torque, and realize the coordinated optimization of path tracking and stability control of the distributed drive vehicle according to the optimal four-wheel driving torque.

[0055] Exemplarily, as Figure 4 shown, this embodiment provides an optimization method for path tracking and stability control of a distributed drive vehicle, including the following steps: Identify the road surface type based on visual information and lidar point cloud data, and estimate the tire cornering stiffness range based on the road surface type; Based on the pre - estimated vehicle sideslip angle at the center of mass, combined with the dynamic output feedback model predictive control algorithm, the controller gain coefficients and observation error weights corresponding to the estimated state sequences and estimated error boundary sequences at different times are obtained; the optimal control gain is obtained by querying a two - dimensional table model, and the two - dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence and the control gain; based on the optimal control gain and the tire cornering stiffness range, the optimal control quantities are calculated, and the optimal control quantities include the four - wheel steering angles and the additional yaw moment. Based on the four - wheel steering angles and the additional yaw moment, with the goal of minimizing the tire utilization rate, the drive torque is optimized and distributed by using the quadratic programming algorithm to obtain the optimal four - wheel drive torque, so as to realize the cooperative optimization of path tracking and stability control of distributed - drive vehicles.

[0056] In this embodiment, the identification of the road surface type based on visual information and lidar point cloud data includes: The YOLOv8 network model is used to extract features from visual information, and the extracted features are input into a trained classifier to output the first road surface type recognition result and the corresponding first confidence level; The lidar point cloud data is input into a trained BP neural network to output the second road surface type recognition result and the corresponding second confidence level; the specific formula for calculating the confidence level is as follows:

[0057] In the formula, is the confidence level of the output result, is the standard deviation, is the input quantity of the neural network, is the output result of the neural network, is the label value of the road surface type, is a constant; Based on the first road surface type recognition result, the first confidence level, the second road surface type recognition result, and the second confidence level, the fused confidence level and the road surface type corresponding to the fused confidence level are calculated, and the specific formula is as follows:

[0058]

[0059]

[0060] In the formula, is the first road surface type recognition result, is the first confidence level; is the second road surface type recognition result, is the second confidence level; is the road surface type of the final output, equal to the road surface type corresponding to the fusion confidence; is the fusion confidence; is the excitation factor; is the penalty factor.

[0061] In this embodiment, the range of tire cornering stiffness estimated based on the road surface type includes: Obtain the road surface adhesion coefficient based on the road surface type; Input the road surface adhesion coefficient, tire cornering angle, and wheel vertical force into a composite tire model that combines the brush tire model and the magic formula to output the range of tire cornering stiffness; among them, the basic model of the composite tire model uses a BP neural network; The specific expression of the composite tire model is as follows:

[0062] In the formula, is the tire lateral force, is the cornering stiffness, is the road surface adhesion coefficient, is the tire cornering angle, is the tangent function, sin is the sine function, arctan is the arctangent function, is the absolute value of, are respectively different fitting coefficients of the magic tire, , representing the fitting stiffness coefficient, is the wheel vertical force, is the tire cornering angle threshold, is the correction parameter.

[0063] In this embodiment, based on the pre-estimated vehicle center of mass cornering angle, combined with the dynamic output feedback model predictive control algorithm, the controller gain coefficients and observation error weights corresponding to the estimated state sequences and estimated error boundary sequences at different times are obtained; the optimal control gain is obtained by querying through a two-dimensional table model, including: Based on the pre-estimated vehicle center of mass cornering angle, combined with the dynamic output feedback model predictive control algorithm, calculate the control gains and observation error weights corresponding to the estimated state sequences and estimated error boundary sequences at different times; Construct a two-dimensional table model based on the estimated state sequences and estimated error boundary sequences; According to the current estimated state and estimated error boundary, query the two-dimensional table model to obtain the controller gain coefficients and observation error weights; Calculate the optimal control gain according to the obtained controller gain coefficients and observation error weights.

[0064] In this embodiment, the optimal control quantity is calculated based on the optimal control gain and the tire cornering stiffness range. The optimal control quantity includes the four-wheel steering angles and the additional yaw moment, and the method comprises: According to the tire cornering stiffness range, the vertices of a pre-constructed LPV model are dynamically adjusted to reduce the uncertain boundary of the LPV model, and the scheduling parameter for solving the optimal control quantity is calculated. The LPV model is used to reflect the dynamic motion state of the vehicle under the current road scenario; Based on the optimal control gain, the current estimated state variables, and the scheduling parameter, the optimal control quantity is calculated. The specific formula is as follows:

[0065] In the formula, is the optimal control quantity; is the scheduling parameter; is the current estimated state variable; is the optimal control gain.

[0066] In this embodiment, the specific expression of the LPV model is as follows:

[0067] In the formula, is the maximum vehicle speed, is the minimum vehicle speed, is the maximum cornering stiffness of the front axle, is the minimum cornering stiffness of the front axle, is the maximum cornering stiffness of the rear axle, is the minimum cornering stiffness of the rear axle; respectively represent different reference variables, where j takes the value of 1 or 2.

[0068] In this embodiment, based on the four-wheel steering angles and the additional yaw moment, with the goal of minimizing the tire utilization rate, the drive torque is optimized and distributed using the quadratic programming algorithm to obtain the optimal four-wheel drive torque, and the method comprises: With the minimum tire utilization rate as the optimization objective J, the drive torque is optimized and distributed based on the quadratic programming algorithm, and the optimal four-wheel drive torque is calculated. The specific expression is as follows:

[0069] In the formula, are the longitudinal tire force and the vertical tire force respectively; is the road surface adhesion coefficient; Constraint conditions:

[0070] In the formula, The tire driving forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively, is the front wheel angle of the four-wheel steering angle, is the wheelbase of the vehicle in front, is the wheel radius, is the additional yaw moment, is the peak torque of the in-wheel motor, is the total longitudinal driving torque.

[0071] Such as Figure 5 shown, this embodiment also provides a distributed drive vehicle path tracking and stability control optimization system, including: an estimation module, configured to identify the road surface type based on visual information and lidar point cloud data, and estimate the tire cornering stiffness range based on the road surface type; a first calculation module, configured to obtain the controller gain coefficient and the observation error weight corresponding to the estimated state sequence and the estimated error boundary sequence at different times based on the pre-estimated vehicle center of mass sideslip angle, in combination with the dynamic output feedback model predictive control algorithm; obtain the optimal control gain by querying through a two-dimensional table model, where the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence, and the control gain; calculate the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, where the optimal control quantity includes the four-wheel steering angle and the additional yaw moment; a second calculation module, configured to optimize the distribution of the driving torque by using the quadratic programming algorithm with the minimum tire utilization rate as the goal based on the four-wheel steering angle and the additional yaw moment, and obtain the optimal four-wheel driving torque to achieve the collaborative optimization of the distributed drive vehicle path tracking and stability control.

[0072] The present invention also provides a device, including: a memory, configured to store a computer program; a processor, configured to implement the steps of the distributed drive vehicle path tracking and stability control optimization method when executing the computer program.

[0073] When the processor executes the computer program, it implements the steps for optimizing the path tracking and stability control of the distributed drive vehicle, such as: identifying the road surface type based on visual information and lidar point cloud data, and estimating the tire cornering stiffness range based on the road surface type; based on the pre-estimated vehicle centroid side slip angle, combining with the dynamic output feedback model predictive control algorithm, obtaining the controller gain coefficients and observation error weight corresponding to the estimated state sequence and estimated error boundary sequence at different times; querying the optimal control gain through a two-dimensional table model, where the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence and the control gain; calculating the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, where the optimal control quantity includes the four-wheel steering angles and the additional yaw moment; based on the four-wheel steering angles and the additional yaw moment, with the goal of minimizing the tire utilization rate, using the quadratic programming algorithm to optimize the distribution of the driving torque, obtaining the optimal four-wheel driving torque, so as to realize the collaborative optimization of the path tracking and stability control of the distributed drive vehicle.

[0074] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, such as: an estimation module, which is used to identify the road surface type based on visual information and lidar point cloud data, and estimate the tire cornering stiffness range based on the road surface type; a first calculation module, which is used to obtain the controller gain coefficients and observation error weight corresponding to the estimated state sequence and estimated error boundary sequence at different times based on the pre-estimated vehicle centroid side slip angle, combining with the dynamic output feedback model predictive control algorithm; querying the optimal control gain through a two-dimensional table model, where the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence and the control gain; calculating the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, where the optimal control quantity includes the four-wheel steering angles and the additional yaw moment; a second calculation module, which is used to optimize the distribution of the driving torque using the quadratic programming algorithm based on the four-wheel steering angles and the additional yaw moment with the goal of minimizing the tire utilization rate, obtaining the optimal four-wheel driving torque, so as to realize the collaborative optimization of the path tracking and stability control of the distributed drive vehicle.

[0075] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the distributed drive vehicle path tracking and stability control optimization device. For example, the computer program may be divided into an estimation module, a first calculation module, and a second calculation module; the estimation module is configured to identify the road surface type based on visual information and lidar point cloud data, and estimate the tire cornering stiffness range based on the road surface type; the first calculation module is configured to obtain the controller gain coefficient and the observation error weight corresponding to the estimated state sequence and the estimated error boundary sequence at different times based on the pre-estimated vehicle centroid side slip angle and in combination with the dynamic output feedback model predictive control algorithm; obtain the optimal control gain by querying a two-dimensional table model, where the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence, and the control gain; calculate the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, where the optimal control quantity includes the four-wheel steering angles and the additional yaw moment; the second calculation module is configured to optimize the distribution of the driving torque by using a quadratic programming algorithm with the goal of minimizing the tire utilization rate based on the four-wheel steering angles and the additional yaw moment, so as to obtain the optimal four-wheel driving torque, thereby realizing the collaborative optimization of the distributed drive vehicle path tracking and stability control.

[0076] The distributed drive vehicle path tracking and stability control optimization device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The distributed drive vehicle path tracking and stability control optimization device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above are examples of the distributed drive vehicle path tracking and stability control optimization device, and do not constitute a limitation on the distributed drive vehicle path tracking and stability control optimization device. It may include more components than the above, or combine some components, or different components. For example, the distributed drive vehicle path tracking and stability control optimization device may further include input / output devices, network access devices, a bus, etc.

[0077] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The processor is the control center of the distributed drive vehicle path tracking and stability control optimization, and connects various parts of the entire distributed drive vehicle path tracking and stability control optimization device through various interfaces and circuits.

[0078] The memory can be used to store the computer program and / or module. The processor realizes various functions of the distributed drive vehicle path tracking and stability control optimization device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0079] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0080] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the path tracking and stability control of a distributed drive vehicle are implemented.

[0081] If the modules / units integrated in the distributed drive vehicle path tracking and stability control optimization system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0082] Based on such understanding, all or part of the processes in the above-mentioned distributed drive vehicle path tracking and stability control optimization method of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned distributed drive vehicle path tracking and stability control optimization method can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or preset intermediate form, etc.

[0083] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0084] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0085] The above-mentioned embodiments are only one of the implementation manners capable of realizing the technical solution of the present invention. The scope of protection required by the present invention is not only limited by this embodiment, but also includes any changes, substitutions and other implementation manners that are easily conceivable by those skilled in the art within the technical scope disclosed by the present invention.

[0086] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still can modify the specific implementation manner of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An optimization method for path tracking and stability control of a distributed drive vehicle, characterized in that, Including: Identifying the road surface type based on visual information and lidar point cloud data, and estimating the tire cornering stiffness range based on the road surface type; Based on the pre-estimated vehicle centroid sideslip angle, combined with the dynamic output feedback model predictive control algorithm, obtaining the controller gain coefficients and observation error weights corresponding to the estimated state sequence and the estimated error boundary sequence at different times; querying the optimal control gain through a two-dimensional table model, where the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence, and the control gain; calculating the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, where the optimal control quantity includes the four-wheel steering angles and the additional yaw moment; Based on the four-wheel steering angles and the additional yaw moment, aiming at minimizing the tire utilization rate, optimizing the distribution of the driving torque using the quadratic programming algorithm to obtain the optimal four-wheel driving torque, so as to realize the collaborative optimization of path tracking and stability control of a distributed drive vehicle.

2. The optimized method for path tracking and stability control of a distributed drive vehicle according to claim 1, characterized in that The identifying the road surface type based on visual information and lidar point cloud data includes: Using the YOLOv8 network model to extract features from the visual information, inputting the extracted features into the trained classifier, and outputting the first road surface type recognition result and the corresponding first confidence level; Inputting the lidar point cloud data into the trained BP neural network, and outputting the second road surface type recognition result and the corresponding second confidence level; the specific formula for calculating the confidence level is as follows: In the formula, is the confidence level of the output result, is the standard deviation, is the input of the neural network, is the output result of the neural network, is the pavement type label value, is a constant; Based on the first road surface type recognition result, the first confidence level, the second road surface type recognition result, and the second confidence level, calculating the fused confidence level and the road surface type corresponding to the fused confidence level, and the specific formula is as follows: Wherein, is the recognition result of the first road surface type, is the first confidence level; is the recognition result of the second road surface type, is the second confidence level; is the finally output road surface type, which is equal to the road surface type corresponding to the fused confidence level; is the fused confidence level; is the excitation factor; is the penalty factor.

3. The optimized method for path tracking and stability control of a distributed drive vehicle according to claim 1, characterized in that, The estimating the tire cornering stiffness range based on the road surface type includes: Obtaining the road surface adhesion coefficient based on the road surface type; Inputting the road surface adhesion coefficient, the tire sideslip angle, and the wheel vertical force into a composite tire model combining the brush tire model and the magic formula, and outputting the tire cornering stiffness range; where the basic model of the composite tire model uses a BP neural network; The specific expression of the composite tire model is as follows: In the formula, is the lateral force of the tire, is the cornering stiffness, is the road surface adhesion coefficient, is the tire slip angle, is the tangent function, sin is the sine function, arctan is the arctangent function, is the absolute value of, are the different fitting coefficients of the magic tire, , representing the fitting stiffness coefficient, is the wheel vertical force, is the tire slip angle threshold, is the correction parameter.

4. The optimized method for path tracking and stability control of a distributed drive vehicle according to claim 1, characterized in that, Based on the pre-estimated vehicle centroid sideslip angle, combined with the dynamic output feedback model predictive control algorithm, obtaining the controller gain coefficients and observation error weights corresponding to the estimated state sequence and the estimated error boundary sequence at different times; Querying the optimal control gain through a two-dimensional table model includes: Based on the pre-estimated vehicle centroid sideslip angle, combined with the dynamic output feedback model predictive control algorithm, calculating the control gain and the observation error weights corresponding to the estimated state sequence and the estimated error boundary sequence at different times; Constructing a two-dimensional table model based on the estimated state sequence and the estimated error boundary sequence; According to the current estimated state and the estimated error boundary, querying the two-dimensional table model to obtain the controller gain coefficients and the observation error weights; Calculating the optimal control gain based on the queried controller gain coefficients and observation error weights.

5. The optimized method for path tracking and stability control of a distributed drive vehicle according to claim 1, characterized in that The calculating the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, where the optimal control quantity includes the four-wheel steering angles and the additional yaw moment, includes: According to the tire cornering stiffness range, the vertices of the pre-constructed LPV model are dynamically adjusted to reduce the uncertain boundary of the LPV model, and the scheduling parameters for solving the optimal control quantity are calculated; the LPV model is used to reflect the dynamic motion state of the vehicle under the current road scenario. Based on the optimal control gain, the current estimated state variables, and the scheduling parameters, the optimal control quantity is calculated. The specific formula is as follows: In the formula, is the optimal control quantity; is the scheduling parameter; is the current estimated state variable; is the optimal control gain.

6. The distributed drive vehicle path tracking and stability control optimization method according to claim 5, characterized in that The specific expression of the LPV model is as follows: In the formula, is the maximum vehicle speed, is the minimum vehicle speed, is the maximum cornering stiffness of the front axle, is the minimum cornering stiffness of the front axle, is the maximum cornering stiffness of the rear axle, is the minimum cornering stiffness of the rear axle; respectively represent different reference variables, where j takes the value of 1 or 2.

7. The optimized method for path tracking and stability control of a distributed drive vehicle according to claim 1, characterized in that, Based on the four-wheel steering angles and the additional yaw moment, with the goal of minimizing the tire utilization rate, the driving torque is optimized and distributed using the quadratic programming algorithm to obtain the optimal four-wheel driving torque, including: Taking the minimum tire utilization rate as the optimization objective J, the driving torque is optimized and distributed based on the quadratic programming algorithm, and the optimal four-wheel driving torque is calculated. The specific expression is as follows: wherein, are the longitudinal tire force and the vertical tire force respectively; is the road surface adhesion coefficient; Constraint conditions: In the formula, are the tire driving forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively, is the front wheel angle of the four-wheel steering angle, is the wheelbase of the vehicle in front, is the wheel radius, is the additional yaw moment, is the peak torque of the in-wheel motor, is the total longitudinal driving torque.

8. A distributed drive vehicle path tracking and stability control optimization system, characterized in that, Including: An estimation module for identifying the road surface type based on visual information and lidar point cloud data, and estimating the tire cornering stiffness range based on the road surface type. A first calculation module for obtaining the controller gain coefficients and the observation error weights corresponding to the estimated state sequence and the estimated error boundary sequence at different times based on the pre-estimated vehicle center of mass sideslip angle and combining the dynamic output feedback model predictive control algorithm; querying the optimal control gain through a two-dimensional table model, where the two-dimensional table model is used to reflect the mapping relationship between the estimated state sequence, the estimated error boundary sequence, and the control gain; calculating the optimal control quantity based on the optimal control gain and the tire cornering stiffness range, and the optimal control quantity includes the four-wheel steering angles and the additional yaw moment. A second calculation module for optimizing and distributing the driving torque using the quadratic programming algorithm with the goal of minimizing the tire utilization rate based on the four-wheel steering angles and the additional yaw moment to obtain the optimal four-wheel driving torque, so as to achieve the collaborative optimization of path tracking and stability control of the distributed drive vehicle.

9. An electronic device, characterized in that, Including: A memory for storing computer programs. A processor for implementing the steps of the distributed drive vehicle path tracking and stability control optimization method according to any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the distributed drive vehicle path tracking and stability control optimization method according to any one of claims 1-7.

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