MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation

Abstract

This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: 1) To enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; 2) An improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion;3) The RBFNN and composite nonlinear feedback (CNF)-based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-Matlab simulations.Index Terms-Path tracking, autonomous vehicles, sliding mode control, extended Kalman filter, neural network. I. INTRODUCTIONA UTONOMOUS ground vehicles (AGVs) have attracted large amounts of attention and investment in both the automotive academia and industry over the past decade, due to the gradually increasing demands for the better safety and efficiency in the mobility [1]-[3]. The emergence of AGVs is intended for improving the transportation efficiency, utilization, and most importantly, road security. Rapid advancement in the vehicular cyber-physical system (CPS) and

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