Robust state estimation for a class of nonlinear systems: Fuzzy-model-based LMI approach

In this paper, we investigate the robust state estimation problem for a class of nonlinear dynamic systems described by Takagi-Sugeno (T-S) models. The main contribution of the proposed approach is to reconstruct the T-S models with unmeasurable premise variables into the uncertain T-S models and introduce the estimated state feedback to the models. The sufficient conditions for the convergence of the state estimation error are obtained based on the Lyapunov stability theory, and presented in terms of Linear Matrix Inequalities (LMIs). Finally, a numerical example is given to illustrate the effectiveness of the proposed approach.