Hoo Robust Fuzzy Control for Nonlinear Dynamic Systems

Abstract

Abstract: Hrobust fuzzy control systems have received a substantial amount of attention in the control research. This is due to the potential benefits of mixing a knowledge-base system representing the "fuzzy systems" with the well-established theorems in the area of "H" robust control". This has resulted in a knowledge-based robust control system that is governed by rules in terms of describing the control aspects of a system operation. In this sense, this thesis has presented two main issues related to H™ robust fuzzy control of nonlinear dynamical systems. The first has been fuzzy modeling of nonlinear dynamical systems, whereas the second was directed towards H fuzzy gain-scheduling control systems. Regarding fuzzy modeling, that was achieved by employing Takagi-Sugeno (T-S) fuzzy modeling technique. The employed (T-S) fuzzy modeling technique was able to cluster the entire nonlinear global model into linear sub-models that are governed by fuzzy rules. (T-S) fuzzy modeling has a number of potentials with respect to other fuzzy modeling techniques; that is a small number of rules are obtained even for complicated multivariable systems. With respect to the H fuzzy gain-scheduling, the thesis has first presented an approach for designing H fuzzy controller for disturbance rejection via defining a suitable LYAPUNOV potential function of the fuzzy model. Hence designing a controller by reducing the problem to a standard Linear Matrix Inequalities (LMI) formulation. H" fuzzy gain-scheduling has been achieved via treating the (T-S) fuzzy sub-models as a Linear Parameter Varying (LPV) system. Hence synthesizing a scheduling controller for variation in parameters. The novel idea of H fuzzy gain-scheduling has been based on sensing variations in system parameters, adapting the LMI designed controller, while preserving the robustness character of the controller in phase of parametric variation.