Path tracking control for autonomous vehicles with saturated input: A fuzzy fixed-time learning control approach

Abstract

This paper presents a fuzzy fixed-time learning path tracking control strategy for autonomous vehicles with saturated input and uncertainties. First, Takagi-Sugeno (T-S) model is designed to realize an accurate description of the nonlinear path tracking systems. The T-S model is the weighted sum of a series of linear dynamics models. The weighting coefficients are described by fuzzy membership functions. Then, with the consideration of the saturated input, a fixed-time learning control is proposed. It contains a learning law to estimate the uncertainties of the system on-line. The fixed-time convergence and the robustness properties are analyzed through Lyapunov stability theory. Finally, the effectiveness of the proposed control method has been verified through the CarSim/Simulink co-simulations under different driving conditions. The results demonstrate that the developed controller significantly improves the performance of path tracking and follows the desired path well even in the case of parameter uncertainties and external disturbances.