Design of robust adaptive observer for nonlinear TS fuzzy system with unknown inputs
This paper deals with robust estimation of states and unknown inputs for a class of non linear system described by Takagi-Sugeno fuzzy models. The proposed approach is developed on the basis of adaptive Luenberger observer theory. A robustness study, using the L 2 technique, is carried out to take in consideration the model uncertainties. Unknown inputs estimation is ensured through a fuzzy proportional integral adaptive law, computed from an accurate observer outputs which allow a fast dynamic estimation. Design conditions are formulated into Linear Matrix Inequalities (LMIs) terms. Numerical example is given to illustrate the efficiency of the proposed techniques.
This chapter is devoted to problems of the control of an observation process under various assumptions concerning the measurement matrix and the intensity of noise. A general procedure for constructing optimal observation laws is given. Specific procedures are obtained and investigated for particular cases.