Observer-based Adaptive Sliding Mode Control of Autonomous Vehicle Rollover Behavior Combing with Markovian Switching

Abstract

This paper proposes a novel observer-based sliding mode control (SMC) to enhance the performance of autonomous vehicles (AVs) rollover behavior under various road profile input. The model of half-car system is first established to describe the AVs rollover behavior by considering nonlinear dynamics of tire force and controllable suspension force under various movement conditions. Moreover, an unscented Kalman Filter (UKF) algorithm is proposed to identify the sprung mass. Combing with the interacting multiple model (IMM) approach and Markov Chain Monte Carlo (MCMC) theory, a novel interacting multiple model unscented Kalman Filters (IMMUKF) observer based is developed to estimate the movement state of AVs system. Then, an adaptive observer-based sliding mode control (AOSMC) strategy is proposed to constrain the AVs roll performance under the various external input. The stability of the proposed algorithm is proved by using Lyapunov function. Finally, simulations and validations are performed on a high-fidelity CarSim® software by using J-turn scenario under various road excitation, to validate the proposed algorithm for AVs system, and the results illustrate that the improved roll states are more than 15% compared with the traditional SMC algorithm.