A robust state estimation for a class of uncertain linear time-invariant descriptor systems

This paper proposes a robust state observer for linear time-invariant descriptor systems in which parametric uncertainty exists in the derivative, the system, and the input matrices of the system. The proposed approach is based on a new parameterization in state variables such that in the new model, the derivative matrix is known. An observer is suggested for estimating the state of the new model. Sufficient conditions are obtained for the convergence of the observer in the form of a linear matrix inequality (LMI), which can be solved by the YALMIP toolbox. Numerical examples accompanied by comparison are presented to demonstrate the efficient performance of the proposed observer.