4wis-4wid integrated control method based on adhesion coefficient

Abstract

The application discloses a 4WIS-4WID integrated control method based on an attachment coefficient, which constructs an integrated control framework with a road surface attachment coefficient as the core in the integrated control of a four-wheel independent steering and a four-wheel independent driving system. The framework takes a steering trajectory tracking controller and a driving stability controller as collaborative subjects, realizes information interaction and function collaboration of steering and driving through global sharing of the attachment coefficient, and forms a "turning angle-torque" two-dimensional regulation and control mode. The designed integrated controller realizes further optimization in tracking accuracy and lateral stability, and fully embodies the technical advantages of multi-degree-of-freedom collaborative regulation and control.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving, and in particular to a 4WIS-4WID integrated control method based on the adhesion coefficient. Background Technology

[0002] With the rapid development of automotive electrification and intelligentization technologies, distributed drive electric vehicles have become a research hotspot in the field of vehicle dynamics control. Four-wheel independent steering (4WIS) and four-wheel independent driving (4WID) technologies, as the two core supports of distributed drive configurations, provide unprecedented regulatory redundancy for vehicle dynamics control by decoupling the steering and driving degrees of freedom of each wheel. However, the complex nonlinear coupling characteristics between the steering and drive systems make it a key scientific problem that urgently needs to be solved in this field: how to fully utilize the advantages of multi-degree-of-freedom control while simultaneously ensuring trajectory tracking accuracy and overall vehicle stability. Summary of the Invention

[0003] The purpose of this invention is to address the strong nonlinear dynamic coupling between 4WIS and 4WID. On one hand, changes in the steering angle redistribute the vertical load and sideslip angle of each wheel, thus affecting the adhesion limit of the drive wheels and the generation efficiency of differential yaw moment. On the other hand, the differential distribution of drive torque causes an asymmetrical distribution of longitudinal force on the left and right wheels, generating additional yaw moment and altering the tire's sideslip characteristics, which in turn affects the response quality of the steering system. If a strategy architecture of independent steering and drive control is adopted, the coupling effect between the two will be considered as mutual interference, making it difficult to achieve global coordination of control objectives and easily leading to a dilemma where trajectory tracking accuracy and driving stability are mutually constrained. For example, increasing the front wheel steering angle input to improve trajectory tracking accuracy may exacerbate the divergence of the center of gravity sideslip angle; while the differential torque applied to suppress yaw motion may interfere with the path tracking capability of the steering system. To address these problems, constructing a steering-drive integrated control architecture with the road adhesion coefficient as the core operating parameter becomes an effective way to overcome this technical bottleneck. By estimating the road surface adhesion coefficient online in real time, the controller can dynamically acquire the distribution information of the adhesion state of each wheel, and then adaptively adjust the coordinated strategy of four-wheel steering angle and four-wheel torque according to the adhesion conditions. Under high adhesion conditions, the steering system undertakes the main tasks of trajectory tracking and yaw stabilization, while the drive system mainly focuses on longitudinal speed tracking and is supplemented by yaw torque. Under low adhesion conditions, the drive system optimizes torque distribution through the equal adhesion utilization criterion, actively participates in yaw stability regulation, and forms a two-dimensional collaboration with the steering system to jointly maintain the vehicle's trajectory tracking capability and lateral stability margin. This integrated control strategy based on adhesion conditions can fully leverage the multi-degree-of-freedom control advantages of the 4WIS-4WID architecture to achieve a balance between tracking accuracy and stability performance under complex variable adhesion road conditions.

[0004] The present invention solves the above-mentioned technical problems through the following technical means:

[0005] The 4WIS-4WID integrated control method based on the road adhesion coefficient is characterized by the following: The method designs a 4WIS-4WID integrated closed-loop control architecture with real-time perception of the road adhesion coefficient as its core. This architecture uses a UKF estimator as the core of condition perception, and a 4WIS trajectory tracking controller and a 4WID stability controller as the main collaborative control entities. Based on the CarSim vehicle dynamics model, a complete "state perception-decision control-execution feedback" closed-loop control system is constructed, achieving dynamic collaborative optimization of four-wheel steering angle and four-wheel torque, balancing vehicle trajectory tracking accuracy and overall vehicle handling stability across the entire operating range.

[0006] The perception layer of the architecture consists of a UKF road adhesion coefficient estimator and a CarSim state feedback module. The CarSim vehicle model provides real-time feedback on the vehicle's full-dimensional motion state, outputting core state variables for path tracking, including lateral tracking error. and its rate of change Heading angle error and its rate of change It provides closed-loop feedback for trajectory tracking control; on the other hand, it outputs the core state variables of vehicle lateral dynamics, including the sideslip angle. With yaw rate This provides a state-based basis for stability control. The UKF estimator, based on the vehicle's real-time motion state, calculates the road adhesion coefficients for the left front wheel, right front wheel, left rear wheel, and right rear wheel. , , , The real-time online estimation provides key input parameters for the two core controllers to adapt to the operating conditions, which is the foundation for the entire control architecture to achieve adaptive regulation of the road surface.

[0007] The core of the architecture's steering control is a 4WIS trajectory tracking controller based on the road surface adhesion coefficient, consisting of three collaborative sub-modules. The 4WIS mode controller, based on the road surface adhesion coefficient and speed, uses the real-time road surface adhesion coefficient as the primary criterion and combines it with longitudinal vehicle speed to adaptively switch steering modes, laying the foundation for condition-appropriate four-wheel steering angle allocation. The stability controller, based on a split rear-wheel steering system, uses the vehicle's lateral dynamics as feedback and actively intervenes in the rear wheel steering angle to adjust the vehicle's attitude, improving lateral stability margin. The front-wheel steering trajectory tracking controller, based on MPC, uses path tracking error as the optimization target and achieves precise closed-loop control of the front wheel steering angle through rolling time-domain optimization, ensuring the tracking accuracy of the desired path. These three modules collaboratively output the target four-wheel steering angles to the vehicle model and simultaneously synchronize the steering angle control information to the drive control module, realizing information interaction between the steering and drive systems.

[0008] The drive control core of the architecture is a 4WID stability controller based on the road surface adhesion coefficient, employing a hierarchical control architecture. The upper layer is a yaw moment solver based on MPC, which uses the center of gravity sideslip angle and yaw rate as control targets, and combines steering system angle information to calculate in real time the additional yaw moment required to maintain vehicle stability. The lower layer is a four-wheel torque controller based on the road surface adhesion coefficient. Based on the desired yaw moment and the overall vehicle driving force requirements from the upper layer, and combined with the road surface adhesion coefficient of each wheel, it optimizes the distribution of four-wheel drive torque based on the equal adhesion utilization criterion, and outputs the target four-wheel drive torque to the vehicle model, accurately achieving the stability control target while avoiding wheel slippage.

[0009] This technical solution has the following advantages:

[0010] 1. This integrated control architecture can achieve deep synergy between 4WIS steering control and 4WID drive control by sharing global information on road surface adhesion coefficient, thereby solving the pain point of mutual constraints between control objectives in the traditional independent control mode and giving full play to the multi-degree-of-freedom control advantages of 4WIS-4WID electric vehicles. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the 4WIS-4WID integrated control method based on the adhesion coefficient of the present invention. Detailed Implementation

[0012] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments:

[0013] like Figure 1 As shown, this invention presents a 4WIS-4WID integrated control method based on the coefficient of friction. The method is characterized by designing a 4WIS-4WID integrated closed-loop control architecture with real-time perception of the road surface adhesion coefficient as its core. This architecture uses a UKF estimator as the core for condition perception, and a 4WIS trajectory tracking controller and a 4WID stability controller as the main collaborative control entities. Based on the CarSim vehicle dynamics model, a complete "state perception-decision control-execution feedback" closed-loop control system is constructed, achieving dynamic collaborative optimization of four-wheel steering angle and four-wheel torque, balancing vehicle trajectory tracking accuracy and overall vehicle handling stability across the entire operating range.

[0014] like Figure 1 As shown, the perception layer of the architecture consists of a UKF road adhesion coefficient estimator and a CarSim state feedback module. The CarSim vehicle model provides real-time feedback on the vehicle's full-dimensional motion state, outputting core state variables for path tracking, including lateral tracking error. and its rate of change Heading angle error and its rate of change It provides closed-loop feedback for trajectory tracking control; on the other hand, it outputs the core state variables of vehicle lateral dynamics, including the sideslip angle. With yaw rate This provides a state-based basis for stability control. The UKF estimator, based on the vehicle's real-time motion state, calculates the road adhesion coefficients for the left front wheel, right front wheel, left rear wheel, and right rear wheel. , , , The real-time online estimation provides key input parameters for the two core controllers to adapt to the operating conditions, which is the foundation for the entire control architecture to achieve adaptive regulation of the road surface.

[0015] like Figure 1As shown, the main steering control architecture is a 4WIS trajectory tracking controller based on the road surface adhesion coefficient, consisting of three collaborative sub-modules. The 4WIS mode controller, based on the road surface adhesion coefficient and speed, uses the real-time road surface adhesion coefficient as the primary criterion and combines it with longitudinal vehicle speed to adaptively switch steering modes, laying the foundation for condition-appropriate four-wheel steering angle distribution. The stability controller, based on a split rear-wheel steering system, uses the vehicle's lateral dynamics as feedback and actively intervenes in the rear wheel steering angle to adjust the vehicle's attitude, improving the vehicle's lateral stability margin. The front-wheel steering trajectory tracking controller, based on MPC, uses path tracking error as the optimization target and achieves precise closed-loop control of the front wheel steering angle through rolling time-domain optimization, ensuring the tracking accuracy of the desired path. These three components collaboratively output the target four-wheel steering angles to the vehicle model and simultaneously synchronize the steering angle control information to the drive control module, realizing information interaction between the steering and drive systems.

[0016] like Figure 1 As shown, the main drive control component of the architecture is a 4WID stability controller based on the road surface adhesion coefficient, employing a hierarchical control architecture. The upper layer is a yaw moment solver based on MPC, which uses the center of gravity sideslip angle and yaw rate as control targets, and combines steering system angle information to calculate in real time the additional yaw moment required to maintain vehicle stability. The lower layer is a four-wheel torque controller based on the road surface adhesion coefficient. Based on the desired yaw moment and the overall vehicle drive force requirements from the upper layer, and combined with the road surface adhesion coefficient of each wheel, it optimizes the distribution of four-wheel drive torque based on the equal adhesion utilization criterion, and outputs the target drive torque of the four wheels to the vehicle model, accurately achieving the stability control target while avoiding wheel slippage.

Claims

1. A 4WIS-4WID integrated control method based on adhesion coefficient, characterized in that: The proposed method designs a 4WIS-4WID integrated closed-loop control architecture with real-time perception of road surface adhesion coefficient as its core. This architecture uses a UKF estimator as the core for condition perception, and a 4WIS trajectory tracking controller and a 4WID stability controller as the main collaborative control components. Based on the CarSim vehicle dynamics model, it constructs a complete "state perception-decision control-execution feedback" closed-loop control system, achieving dynamic collaborative optimization of four-wheel steering angle and four-wheel torque, balancing vehicle trajectory tracking accuracy and overall vehicle handling stability across the entire operating range.

2. The 4WIS-4WID integrated control method based on adhesion coefficient according to claim 1, characterized in that: The perception layer of the architecture consists of a UKF road adhesion coefficient estimator and a CarSim state feedback module. The CarSim vehicle model provides real-time feedback on the vehicle's full-dimensional motion state, outputting core state variables for path tracking, including lateral tracking error. and its rate of change Heading angle error and its rate of change It provides closed-loop feedback for trajectory tracking control; on the other hand, it outputs the core state variables of vehicle lateral dynamics, including the sideslip angle. With yaw rate This provides a state-based basis for stability control. The UKF estimator, based on the vehicle's real-time motion state, calculates the road adhesion coefficients for the left front wheel, right front wheel, left rear wheel, and right rear wheel. , , , The real-time online estimation provides key input parameters for the two core controllers to adapt to the operating conditions, which is the foundation for the entire control architecture to achieve adaptive regulation of the road surface.

3. The 4WIS-4WID integrated control method based on adhesion coefficient according to claim 1, characterized in that: The core of the architecture's steering control is a 4WIS trajectory tracking controller based on the road surface adhesion coefficient, consisting of three collaborative sub-modules. The 4WIS mode controller, based on the road surface adhesion coefficient and speed, uses the real-time road surface adhesion coefficient as the primary criterion and combines it with longitudinal vehicle speed to adaptively switch steering modes, laying the foundation for condition-appropriate four-wheel steering angle allocation. The stability controller, based on a split rear-wheel steering system, uses the vehicle's lateral dynamics as feedback and actively intervenes in the rear wheel steering angle to adjust the vehicle's attitude, improving lateral stability margin. The front-wheel steering trajectory tracking controller, based on MPC, uses path tracking error as the optimization target and achieves precise closed-loop control of the front wheel steering angle through rolling time-domain optimization, ensuring the tracking accuracy of the desired path. These three modules collaboratively output the target four-wheel steering angles to the vehicle model and simultaneously synchronize the steering angle control information to the drive control module, realizing information interaction between the steering and drive systems.

4. The 4WIS-4WID integrated control method based on adhesion coefficient according to claim 1, characterized in that: The drive control core of the architecture is a 4WID stability controller based on the road adhesion coefficient, which adopts a hierarchical control architecture. The upper layer is a yaw moment solver based on MPC, which uses the center of gravity sideslip angle and yaw rate as control targets, and combines the steering system angle information to calculate the additional yaw moment required to maintain vehicle stability in real time. The lower layer is a four-wheel torque controller based on the road surface adhesion coefficient. Based on the upper layer's desired yaw moment and the vehicle's driving force requirements, and combined with the road surface adhesion coefficient of each wheel, it optimizes the distribution of the four-wheel drive torque based on the equal adhesion utilization rate criterion, and outputs the target four-wheel drive torque to the vehicle model. While avoiding wheel slippage, it accurately achieves the stability control target.

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