Fault Estimation for Uncertain Nonlinear Networked Control Systems

ABSTRACT

This paper proposes the design of a robust fault estimator for a class of nonlinear uncertain NCSs that ensures the fault estimation error is less than prescribed H􀀁 performance level, irrespective of the uncertainties and networkinduced effects. T-S fuzzy models are firstly employed to describe the nonlinear plant. Markov processes are used to model these random network-induced effects. Sufficient conditions for the existence of such a fault estimator for this class of NCSs are derived in terms of the solvability of bilinear matrix inequalities. An iterative algorithm is proposed to change this non-convex problem into quasi-convex optimization problems, which can be solved effectively by available mathematical tools. The effectiveness of the proposed design methodology is verified by a numerical example.