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Synthèse de contrôleurs prédictifs auto-adaptatifs pour l'optimisation des performances des systèmes

Marwa Turki 1
1 Pôle Automatique et Systèmes
IRSEEM - Institut de Recherche en Systèmes Electroniques Embarqués
Abstract : Even though predictive control uses concrete parameters, the value of these latter has a strong impact on the obtained performances from the system to be controlled. Their tuning is not trivial. That is why the literature reports a number of adjustment methods. However, these ones do not always guarantee optimal values. The goal of this thesis is to propose an analytical and original tuning tuning approach of these parameters. Initially applicable to linear MIMO systems, the proposed approach has been extended to non-linear systems with or without constraints and for which a Takagi-Sugeno (T-S) model exists. The class of nonlinear systems considered here is written in quasi-linear parametric form (quasi-LPV). Assuming that the system is controllable and observable, the proposed method guarantees the optimal stability of this closed-loop system. To do this, it relies, on the one hand, on a conditioning improving technique of the Hessian matrix and, on the other hand, on the concept of effective rank. It also has the advantage of requiring a lower computational load than the approaches identified in the literature. The interest of the proposed approach is shown through the simulation on different systems of increasing complexity. The work carried out has led to a self-adaptive predictive control strategy called "ATSMPC" (Adaptive Takagi-Sugeno Model-based Predictive Control).
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Marwa Turki. Synthèse de contrôleurs prédictifs auto-adaptatifs pour l'optimisation des performances des systèmes. Automatique / Robotique. Normandie Université, Université de Rouen, 2018. Français. ⟨tel-02408037⟩

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