Two-level LPV Model based Sliding Mode Predictive Control with Actuator Input Delay for Vehicle Yaw Stability

2022-01-1111

03/29/2022

Authors Abstract
Content
Active front steering (AFS) system is a new technology to improve vehicle yaw stability. The performance of the vehicle yaw stability directly influences the active safety and handling stability of the vehicle. For the improvement of the vehicle yaw stability, this paper studies the control problem of the AFS system with actuator input delay. A novel two-level linear parameter varying model based sliding mode predictive control strategy is proposed in this paper, which aiming at the AFS system with nonlinearities, actuator input delay and system constraints. Firstly, considering the nonlinearities of the vehicle system, a linear parameter varying vehicle system model with two-level structure is proposed to capture the vehicle dynamic behaviors. Secondly, to deal with the issues of actuator input delay and system constraints, a novel sliding mode predictive control method is put forward. In the process of controller design, a sliding mode control algorithm is employed for the improvement of the robustness of the control system, and then a model predictive control algorithm is employed to deal with system constraints. Thirdly, for the improvement of the feasibility of the proposed controller in the actual system, the objective function of the sliding mode predictive control strategy is optimized by using the particle swarm optimization algorithm. Finally, the joint simulations of Carsim and Matlab/Simulink are conducted to validate the performance of the controller. Experimental results show that the proposed control strategy can effectively deal with the actuator input delay and performs better in ensuring the yaw stability of the vehicle.
Meta TagsDetails
Citation
zhang, y., Xie, Z., Wong, P., and Zhao, J., "Two-level LPV Model based Sliding Mode Predictive Control with Actuator Input Delay for Vehicle Yaw Stability," SAE Technical Paper 2022-01-1111, 2022, .
Additional Details
Publisher
Published
Mar 29, 2022
Product Code
2022-01-1111
Content Type
Technical Paper
Language
English