Commande Prédictive pour le Véhicule Autonome

Abstract : The thesis work contained in this manuscript is dedicated to the Advanced Driving Assistance Systems, which has become nowadays a strategic research line in many car companies. This kind of systems can be seen as a first generation of assisted or semi-autonomous driving, that will set the way to fully automated vehicles. The first part focuses on the analysis and control of lateral dynamics control applications - Autosteer by target tracking and the Lane Centering Assistance System (LCA). In this framework, safety plays a key role, bringing into focus the application of different constrained control techniques for linear parametervarying (LPV) models. Model Predictive Control (MPC) and Interpolation Based Control (IBC) have been the selected ones in the present work. In addition, it is a critical feature to design robust control systems that ensure a correct behavior under system's variation of parameters or in the presence of uncertainty. Robust Positive Invariance (RPI) theory tools are considered to design robust LPV control strategies with respect to large vehicle speed variations and curvature of the road changes. The second axis of this thesis is the optimization-based trajectory planning for overtaking and lane change in highways with anti-collision enhancements. To achieve this goal, an exhaustive description of the possible scenarios that may arise is presented, allowing to formulate an optimization problem which maximizes passenger comfort and ensures system constraints' satisfaction.
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Iris Ballesteros Tolosana. Commande Prédictive pour le Véhicule Autonome. Autre [cs.OH]. Université Paris-Saclay, 2018. Français. ⟨NNT : 2018SACLC007⟩. ⟨tel-01863344⟩

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