Advanced Control Design Tools for Autonomous Vehicles

Abstract

This thesis deals with a new robust control design for autonomous vehicles. The goal is to perform lane keeping under various constraints, mainly actuator saturation of the steering system, lateral wind force, incident obstacles and unknown curvatures. To reach this goal, we propose an improved formulation of Parallel Distributed Compensation (PDC) law since it is a nonlinear system feedback state controller. A direct Lyapunov method to ensure the stability and the stabilization of the discrete-time Takagi-Sugeno (T-S) model representing the autonomous vehicle dynamics is suggested. We derived necessary and sufficient stability and stabilization conditions from the quadratic Lyapunov function. These conditions are expressed in terms of strict Linear Matrix Inequalities (LMIs) extracted from the linearization of the Bilinear Matrix Inequalities (BMIs). The vector state is measured entirely by the Luenberger multi-observers to be used in the feedback control. The results of Autonomous vehicle control are presented in this thesis to show the effectiveness of the proposed approach. Simulations are used to validate the theoretical results.