An unknown input observer for Takagi Sugeno descriptor system with unmeasurable premise variable

This paper deals with the state estimation for non linear descriptor systems described by Takagi-Sugeno (TS) which presents an unknown inputs. Indeed, the purpose of design observer is to estimate the state variables as well as the unknown inputs which simultaneously influence the states and the outputs of the system. The idea is to consider model with unmeasurable premise variables when the decision variables are not available to measurement. Therefore, the proposed approach is to design an unknown input observer for TS descriptor perturbed models with measurable variables. Convergence conditions are established with Lyapunov theory and the L2 optimization in order to guarantee the convergence of the state estimation error. These conditions are given in terms of Linear Matrix Inequalities (LMIs). An academic example illustrates the performances of the proposed approach.