Vehicle motion collaborative robust control method considering model uncertainty

Abstract

The invention discloses a vehicle motion cooperative robust control method considering model uncertainty, and relates to the field of vehicle cooperative control. Establishing a two-degree-of-freedom model of the vehicle; a nonlinear tire model is adopted to describe the interaction relation between the tire and the ground, and the lateral force of the wheel is calculated through the nonlinear tire; based on the steering wheel angle of the driver, generating a control tracking reference quantity through a two-degree-of-freedom model; a vehicle state constraint and a control input constraint are set, a control target is defined as a tracking reference quantity, the uncertainty of the model is suppressed, and an error tracking system is constructed; taking minimization of a preset objective function as an optimization objective, solving a nominal track and a nominal control sequence, and introducing a tightened state constraint and a control constraint; designing a robust control law composed of a feedforward component and a feedback component, and generating a control instruction of a rear wheel steering angle and a wheel additional motor torque; and vehicle motion collaborative robust control is realized according to the control instruction. The invention aims to improve the control stability of the vehicle under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of vehicle cooperative control, and more specifically to a vehicle motion cooperative robust control method that takes into account model uncertainties. Background Technology

[0002] Vehicle motion control technology plays a crucial role in improving driving comfort and enhancing stability under complex road conditions, serving as a fundamental pillar for ensuring safe driving and achieving all-weather autonomous driving. However, on icy roads or during emergency turns, tires can easily enter a saturation zone, resulting in insufficient tire force to guarantee safe vehicle operation. Many studies employ torque vector control (TVC) to guide the vehicle to track the desired yaw rate and stabilize the sideslip angle, thereby ensuring overall vehicle stability. However, existing methods have two significant limitations: first, they do not adequately consider the coordination between the vehicle's drive and steering subsystems; second, they do not account for the errors and uncertainties between the control-oriented model and the actual vehicle.

[0003] Among existing vehicle handling stability control methods, patent "CN109398361B" provides a handling stability control method for four-wheel independent drive vehicles. This method designs a vehicle motion controller including a longitudinal controller and a yaw controller, controls the actual angular velocity of the wheels to track the target angular velocity, and coordinates the torque distribution of each motor. This method can improve the handling stability of four-wheel independent drive vehicles; however, it only utilizes the vehicle's drive subsystem. Patent "CN116279409A" invented a cooperative control method for four-wheel independent drive and steering electric vehicles. It constructs an optimization problem based on the current vehicle motion state and the expected value of the yaw rate, solves the optimization problem, and obtains the rear wheel steering angle and additional yaw moment. Although this invention coordinates the vehicle drive and rear wheel steering subsystems, it does not consider the errors and uncertainties between the established mathematical model and the actual vehicle. Summary of the Invention

[0004] In view of this, the present invention provides a vehicle motion cooperative robust control method that considers model uncertainties, aiming to improve the handling stability of vehicles under complex operating conditions. This method simultaneously considers vehicle state constraints, input constraints, and modeling errors.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A cooperative robust control method for vehicle motion considering model uncertainties includes the following steps: A two-degree-of-freedom model of the vehicle is established based on its lateral and yaw motions. A nonlinear tire model is used to describe the interaction between the tire and the ground, and the lateral force of the wheel is calculated using a nonlinear tire model. Based on the driver's steering wheel angle, a control tracking reference quantity is generated using a two-degree-of-freedom model; Set vehicle state constraints and control input constraints, define the control objective as tracking the reference quantity and suppressing model uncertainty, and construct an error tracking system; The nominal trajectory and nominal control sequence are solved by minimizing the preset objective function, and tightened state constraints and control constraints are introduced. Design a robust control law consisting of feedforward and feedback components, and generate control commands for the rear wheel steering angle and the torque of the wheel-attached motor. Robust control of vehicle motion coordination is achieved based on control commands.

[0006] Optionally, a two-degree-of-freedom model of the vehicle can be established based on its lateral and yaw motions, where the two-degree-of-freedom model is represented as follows: ; in Vehicle status. The sideslip angle is the angle of the centroid. This indicates the yaw rate of the vehicle. and These represent the lateral forces of the front and rear wheels, respectively. Let V be the vehicle mass and V be the vehicle speed. and These represent the distances from the front and rear axles to the vehicle's center of gravity, respectively. It is the moment of inertia of the vehicle rotating about its center of mass, and the control variable is... ,in M is the lateral force on the rear wheel, and M is the additional yaw torque. This represents the unmodeled error and satisfies... , These represent the model errors respectively. and The maximum value.

[0007] Optionally, a nonlinear tire model is used to describe the interaction between the tire and the ground. The lateral force of the wheel is calculated using a nonlinear tire model, as shown below: A nonlinear brush tire model is used to calculate the lateral force on the wheel, thereby describing the tire-ground relationship of the vehicle, as shown below: ; in For tire lateral stiffness, Indicates tire lateral stiffness. Indicates the normal vertical load on the tire, and the front wheel slip angle. and rear wheel slip angle The calculation is as follows: ; in, The sideslip angle is the angle of the centroid. This indicates the yaw rate of the vehicle. and These represent the distances from the front and rear axles to the vehicle's center of gravity, respectively, and V is the vehicle's speed. and These refer to the steering angles of the front and rear wheels of the vehicle, respectively.

[0008] Optionally, based on the driver's steering wheel angle, a control tracking reference value is generated using a two-degree-of-freedom model, as shown below: Based on the two-degree-of-freedom model of the vehicle: ; The following reference values ​​are generated: ;

[0009]

[0010]

[0011]

[0012]

[0013] ; in, For vehicle quality, The sideslip angle is the angle of the centroid. For longitudinal vehicle speed, For the front wheel steering angle, For the front wheel lateral stiffness, For rear wheel lateral stiffness, This refers to the front axle wheelbase. This refers to the rear axle wheelbase. The yaw rate is angular velocity. for Reference yaw rate within the domain, for The steering wheel angle of the domain, It is a time constant. for Domain change symbol, For the vehicle's inherent frequency, The damping coefficient is... Wheelbase As a stability factor, This is the yaw rate gain.

[0014] Optionally, vehicle state constraints and control input constraints can be set, as follows: Yaw rate constraint is The reference yaw rate constraint is The sideslip angle constraint is ;in It is the maximum yaw rate. This represents the maximum value of the centroid sideslip angle. This refers to the front axle wheelbase. The rear axle wheelbase; the upper bound of the state is defined as: ; The upper bound of the control input is: ; in This represents the maximum lateral force of the tire. This is the maximum value of the additional yaw torque.

[0015] Optionally, the control objective is to track the reference yaw rate while adhering to state and control constraints, without considering errors. The nominal system is: ; and These are nominal status and control input, respectively; Actual tracking error and nominal tracking error Defined as ; The error tracking system is as follows: ; ; in ; The error between the real vehicle and the established mathematical model is defined as . ,get .

[0016] Optional, objective function as follows:

[0017] in , , This represents the current lateral force. It is a symmetric positive semi-definite matrix. It is a symmetric positive definite matrix. .

[0018] Optionally, the robust control law is defined as follows: ; in It is a positive definite diagonal matrix. and These represent the control sequence and state sequence obtained from solving the optimization problem, respectively.

[0019] Optionally, the formula for calculating the rear wheel steering angle is as follows: ; The additional motor torque is as follows: ; in The distance between the left and right wheels. For the tire radius, To provide the optimal additional yaw torque.

[0020] As can be seen from the above technical solution, compared with the prior art, this invention provides a vehicle motion cooperative robust control method that considers model uncertainty, coordinates the vehicle's four-wheel independent drive and rear-wheel steering subsystems, explicitly considers modeling uncertainty, and solves the system nonlinearity problem caused by the tire model. It designs a composite control law by integrating model predictive control and feedback linearization. Tightened state and control constraints are formulated to ensure robust constraint satisfaction and robust vehicle control even in the presence of uncertainty. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the technical solution provided by the present invention; Figure 2 This is a schematic diagram of the two-degree-of-freedom vehicle model of the present invention; Figure 3 This is a schematic diagram of the yaw rate tracking and center of mass sideslip angle during the DLC scene HIL test of the present invention; Figure 4 This is a schematic diagram of the four-wheel additional motor torque and rear wheel rotation angle in the DLC scene HIL test of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention discloses a vehicle motion cooperative robust control method considering model uncertainties, comprising the following steps: A two-degree-of-freedom model of the vehicle is established based on its lateral and yaw motions. A nonlinear tire model is used to describe the interaction between the tire and the ground, and the lateral force of the wheel is calculated using a nonlinear tire model. Based on the driver's steering wheel angle, a control tracking reference quantity is generated using a two-degree-of-freedom model; Set vehicle state constraints and control input constraints, define the control objective as tracking the reference quantity and suppressing model uncertainty, and construct an error tracking system; The nominal trajectory and nominal control sequence are solved by minimizing the preset objective function, and tightened state constraints and control constraints are introduced. Design a robust control law consisting of feedforward and feedback components, and generate control commands for the rear wheel steering angle and the torque of the wheel-attached motor. Robust control of vehicle motion coordination is achieved based on control commands.

[0025] Figure 1 A block diagram of the proposed control structure is described. First, a two-degree-of-freedom reference model generates an ideal desired yaw rate based on the steering wheel angle turned by the driver, which serves as the reference input for the controller. This invention explicitly considers the model mismatch problem caused by vehicle model and parameter uncertainties. Figure 1 The composite control law consists of two parts. The first part is the feedforward component obtained by solving the Model Predictive Control (MPC) optimization problem. The second part considers the feedback component of model uncertainty. In addition, tightened state and control constraints were defined to ensure robust constraint satisfaction under uncertainties. The main design process is described below: like Figure 2 As shown, Step 1: Model Establishment: 1) Establishment of a two-degree-of-freedom model of the vehicle The present invention first establishes a two-degree-of-freedom model of the vehicle, which takes into account the lateral motion and yaw motion of the vehicle.

[0026] (1); in Vehicle status. The sideslip angle is the angle of the centroid. This indicates the yaw rate of the vehicle. and These represent the lateral forces of the front and rear wheels, respectively. and These represent the distances from the front and rear axles to the vehicle's center of gravity, respectively. It is the moment of inertia of the vehicle rotating about its center of mass; the controlling variables are the lateral force of the rear wheels and the additional yaw torque. . This represents the unmodeled error and satisfies... .

[0027] 2) Tire model

[0028] A nonlinear brush tire model is used to calculate the lateral force on the wheel, thereby describing the tire-ground relationship of the vehicle, as shown below: (2); in Indicates tire lateral stiffness (front wheel is) The rear wheel is ), Indicates the normal vertical load of the tire (front axle is) The rear axle is The tire slip angle is calculated as follows: (3); 3) Tracking Reference To ensure vehicle stability, appropriate reference center of gravity sideslip angle and reference yaw rate need to be generated based on the front wheel steering angle input by the driver, according to the vehicle's two-degree-of-freedom model: (4); The following reference values ​​can be generated: (5); in For vehicle quality, The sideslip angle is the angle of the centroid. For longitudinal vehicle speed, This refers to the steering angle of the front wheels.

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] (6); 4) Vehicle status and actuator constraints Yaw rate constraint is For safety considerations, the reference yaw rate constraint is as follows: Angular constraint is The upper bound of the state is defined as follows: (7); The upper bound of the control input is: (8); 5) Definition of control objectives The control objective is to track the reference yaw rate while adhering to state and control constraints (7) and (8), even in the presence of uncertainties. Errors are not considered. The nominal system is: (9); The actual tracking error and the nominal tracking error are defined as follows: (10); The error tracking system is as follows: (11); (12); in: (13); The error between the real vehicle and the established mathematical model is defined as . ,get: (14); Step 2: Controller Design The scheme coordinates the four-wheel independent drive and rear-wheel steering subsystems of an electric vehicle to solve the nonlinear tracking problem discussed in step 1. This approach encompasses the definition of the optimization problem, the design of robust control laws, and the tightening of control and state constraints.

[0035] 1) Description of the robust optimization problem

[0036] Question 1: (15); Constraints: (16); (17); (18); (19); (20); in (twenty one); (twenty two); Represents the prediction time domain, objective function as follows: (twenty three); in . It is a symmetric positive semi-definite matrix. It is a symmetric positive definite matrix. .

[0037] 2) Robust control law

[0038] Solving problem 1 yields the nominal trajectory. and nominal control sequence However, since computer systems cannot implement continuous control laws, at time... The control law acting on the system is defined as follows: (twenty four); Based on formula (24), the required rear wheel lateral force can be obtained. and additional yaw moment To apply lateral force, the rear wheel angle is efficiently determined using a linear model, while the yaw moment is evenly distributed across multiple motors, generating motor torque.

[0039] 3) Control Action Generation

[0040] According to the tire model (3), the rear wheel steering angle can be obtained as follows: (25); The additional motor torque of the wheel is calculated based on the additional yaw moment as follows: (26); in The distance between the left and right wheels. This is the tire radius.

[0041] To further illustrate the effectiveness of the invention, a driver-in-the-loop simulator was used for verification. The experimental platform integrates a full-view driving simulator with a 180° projection screen (for visual immersion) and a six-degree-of-freedom motion platform (for providing realistic vehicle dynamics feedback). The system architecture comprises three industrial computers (IPCs): IPC1 runs the vehicle dynamics model and provides motion status to the simulator; IPC2 executes the SCANeR program, providing visual cues to the driver; and IPC3, a miniature industrial computer, acts as the controller, executing the proposed control strategy. A laptop computer monitors the entire system and performs controller calibration. Control signals are exchanged between components via a Controller Area Network (CAN) bus, while monitoring signals are forwarded using User Datagram Protocol (UDP).

[0042] To verify the effectiveness and advantages of the method proposed in this invention, Model Predictive Control (MPC) and Backstepping (BS) were selected as comparison methods. For fairness, the Nonlinear Robust Model Predictive Control (NRMPC) method proposed in this invention uses the same weight parameters as the comparison algorithms. , and feedback matrix All driving experiments were conducted by the same driver who had received simulator training before the actual driving.

[0043] Select weight matrix , and feedback matrix Control cycle Predicting the time domain . , , , , .

[0044] The test scenario involved a driver performing a dual lane change (DLC) maneuver on a low-friction surface. Yaw rate tracking curves under three control strategies are shown. Results indicate that the proposed NRMPC and MPC effectively stabilize the vehicle and assist the driver in completing the DLC maneuver. However, when using the BS controller, the driver failed to complete the DLC maneuver, resulting in a significant difference between the vehicle's yaw rate and the expected value. This indicates that the vehicle is out of control and cannot follow the driver's intentions.

[0045] Comparing the yaw rate curves of NRMPC and MPC, it can be seen that NRMPC can accurately and smoothly track the reference yaw rate with minimal overshoot, thus ensuring stability and driving comfort. When the driver performs a steering maneuver, a sudden change in yaw rate is expected. While MPC can stabilize the vehicle, it causes significant overshoot in the yaw rate, affecting driving comfort. The peak tracking error of NRMPC yaw rate is... The maximum overshoot is The peak tracking error of the MPC's yaw rate is... The maximum overshoot reached The peak side slip angle of NRMPC is only Degrees. In contrast, the peak lateral slip angle of MPC is... The degree indicates poor control performance.

[0046] The additional torque applied to the wheels by the motor and the rear wheel steering angle are shown. The additional torque curves reveal significant differences in motor torque among the three algorithms. Although the additional torque of both NRMPC and MPC satisfies the control constraints, the motor torque of NRMPC is significantly lower than that of MPC, indicating a reduction in energy consumption. This demonstrates that the NRMPC proposed in this invention can better coordinate the four-wheel independent drive and rear-wheel steering systems, achieving superior control performance. It is worth noting that because BS relies on feedback linearization, it tends to reach the maximum additional torque, leading to increased energy consumption and a failure to achieve vehicle stability.

[0047] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0048] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A vehicle motion cooperative robust control method considering model uncertainties, characterized in that, Includes the following steps: A two-degree-of-freedom model of the vehicle is established based on its lateral and yaw motions. A nonlinear tire model is used to describe the interaction between the tire and the ground. The lateral force of the wheel is calculated using a nonlinear tire model, as shown below: A nonlinear brush tire model is used to calculate the lateral force on the wheel, thereby describing the tire-ground relationship of the vehicle, as shown below: ； in For tire lateral stiffness, Indicates tire lateral stiffness. Indicates the normal vertical load on the tire, and the front wheel slip angle. and rear wheel slip angle The calculation is as follows: ； in, The sideslip angle is the angle of the centroid. This indicates the yaw rate of the vehicle. and These represent the distances from the front and rear axles to the vehicle's center of gravity, respectively, and V is the vehicle's speed. and These are the front wheel steering angle and the rear wheel steering angle of the vehicle, respectively. Based on the driver's steering wheel angle, a control tracking reference quantity is generated using a two-degree-of-freedom model; Set vehicle state constraints and control input constraints, define the control objective as tracking the reference quantity and suppressing model uncertainty, and construct an error tracking system; The nominal trajectory and nominal control sequence are solved by minimizing the preset objective function, and tightened state constraints and control constraints are introduced. Design a robust control law consisting of feedforward and feedback components, and generate control commands for the rear wheel steering angle and the torque of the wheel-attached motor. Robust control of vehicle motion coordination is achieved based on control commands.

2. The vehicle motion cooperative robust control method considering model uncertainty according to claim 1, characterized in that, A two-degree-of-freedom model of the vehicle is established based on its lateral and yaw motions, and the two-degree-of-freedom model is represented as follows: ； in Vehicle status. The sideslip angle is the angle of the centroid. This indicates the yaw rate of the vehicle. and These represent the lateral forces of the front and rear wheels, respectively. Let V be the vehicle mass and V be the vehicle speed. and These represent the distances from the front and rear axles to the vehicle's center of gravity, respectively. It is the moment of inertia of the vehicle rotating about its center of mass, and the control variable is... ,in M is the lateral force on the rear wheel, and M is the additional yaw torque. This represents the unmodeled error and satisfies... , These represent the model errors respectively. and The maximum value.

3. The vehicle motion cooperative robust control method considering model uncertainty according to claim 1, characterized in that, Based on the driver's steering wheel angle, a control tracking reference quantity is generated using a two-degree-of-freedom model, as follows: Based on the two-degree-of-freedom model of the vehicle: ； The following reference values ​​are generated: ； ； in, For vehicle quality, The sideslip angle is the angle of the centroid. For longitudinal vehicle speed, For the front wheel steering angle, For the front wheel lateral stiffness, For rear wheel lateral stiffness, This refers to the front axle wheelbase. This refers to the rear axle wheelbase. The yaw rate is angular velocity. for Reference yaw rate within the domain, for The steering wheel angle of the domain, It is a time constant. for Domain change symbol, For the vehicle's inherent frequency, The damping coefficient is... Wheelbase As a stability factor, This is the yaw rate gain.

4. The vehicle motion cooperative robust control method considering model uncertainty according to claim 1, characterized in that, Vehicle state constraints and control input constraints are set as follows: Yaw rate constraint is The reference yaw rate constraint is The sideslip angle constraint is ;in It is the maximum yaw rate. This represents the maximum value of the centroid sideslip angle. This refers to the front axle wheelbase. The rear axle wheelbase; the upper bound of the state is defined as: ； The upper bound of the control input is: ； in This represents the maximum lateral force of the tire. This is the maximum value of the additional yaw torque.

5. A vehicle motion cooperative robust control method considering model uncertainty according to claim 1, characterized in that, The control objective is to track the reference yaw rate while adhering to state and control constraints, without considering errors. The nominal system is: ； and These are nominal status and control input, respectively; Actual tracking error and nominal tracking error Defined as ； The error tracking system is as follows: ； ； in ； The error between the real vehicle and the established mathematical model is defined as . ,get 。 6. A vehicle motion cooperative robust control method considering model uncertainty according to claim 1, characterized in that, objective function as follows: ； in , , This represents the current lateral force; It is a symmetric positive semi-definite matrix. It is a symmetric positive definite matrix. .

7. A vehicle motion cooperative robust control method considering model uncertainty according to claim 1, characterized in that, The definition of a robust control law is as follows: ； in It is a positive definite diagonal matrix. and These represent the control sequence and state sequence obtained from solving the optimization problem, respectively.

8. A vehicle motion cooperative robust control method considering model uncertainty according to claim 1, characterized in that, The formula for calculating the rear wheel steering angle is as follows: ； The additional motor torque is as follows: ； in The distance between the left and right wheels. For the tire radius, To provide the optimal additional yaw torque.

Images