Robust Fuzzy Quasi-SMC-Based Steering Control of Autonomous Vehicle Subject to Parametric Uncertainties and Disturbances

Abstract

This article proposes a new formulation for a robust trajectory tracking control law of an autonomous vehicle. Autonomous vehicle navigation highly relies on reliable, robust, and dependable steering mechanism, even under challenging conditions and circumstances. The controller design is based on the higher order quasi-sliding mode control (QSMC) algorithm that provides smooth motion control subjected to steering saturation and curvature constraints. In addition, an adaptive single input fuzzy logic control based on Lyapunov stability theorem is incorporated, which relies on the online estimation of perturbations rather than relying on the requirement of a priori knowledge of the upper bounds of the perturbation. Furthermore, the proposed control scheme exhibits a strong robustness toward the effect of uncertainties like parametric, tire cornering stiffness, surface bonding coefficient, and exogenous noises and disturbances. In addition to that, fuzzy control term offers a fast path-tracking error convergence toward equilibrium condition and reduced steady-state error. The overall control scheme through Lyapunov theory ensures the global asymptotic stability of the autonomous vehicle. Finally, the effectiveness and robustness of the proposed control scheme is demonstrated through numerical simulations MATLAB/SIMULINK platform for linear and nonlinear scenarios. Later, experimental validation is conducted over dSPACE SCALEXIO hardware-in-loop (HIL) platform for trajectory tracking along with the input constraints subjected to parametric uncertainties and disturbances.