Unknown input estimation for nonlinear descriptor systems via LMIs and Takagi-Sugeno models

This paper presents an unknown inputs observer for nonlinear descriptor systems. The approach uses the Takagi-Sugeno representation of the nonlinear model. In order to obtain strict linear matrix inequalities a novel observer structure is given. Thus the conditions can be efficiently solved via convex optimization techniques. A numerical example is provided to illustrate the performance of the proposed approach.