Vehicle stability control strategy for high-speed curves based on mode switching

Abstract

This study aims to design a mode-switching control system based on a non-singular fast terminal sliding mode (NFTSM) algorithm for the lateral stability problem of vehicles driving in high-speed curves. First, a three-degrees-of-freedom model of the vehicle was established, and a predicted lateral load transfer ratio (PLTR) was proposed as a threshold for dividing the lateral stability index based on the conventional PLTR. Second, for the vehicle driving state in which the vehicle does not satisfy the PLTR threshold, the vehicle lateral deflection angle is adjusted based on the NFTSM controller to improve the bending path-tracking accuracy of the vehicle. For the vehicle critical rollover state that satisfies the PLTR threshold, an NFTSM-based additional yaw moment control algorithm is proposed to estimate various unknown disturbance and parameter ingestion terms in the vehicle modeling process using an adaptive radial basis function neural network and adaptively adjust the key parameters of the NFTSM controller. Finally, a joint Carsim-Simulink simulation model was built to verify the control algorithm proposed herein. The simulation results show that the mode-switching control system can improve the vehicle’s yaw and rollover stability in high-speed curves and prevent the occurrence of rollover.