Robust Fault Estimator Design for Uncertain Nonlinear Networked Control Systems

This work proposes the design of a robust fault estimator for a class of nonlinear uncertain networked control systems (NCSs) that ensures the fault estimation error is less than prescribed H∞ performance level, irrespective of parameter uncertainties and network-induced effects. T-S fuzzy models are firstly employed to describe the nonlinear plant. Markov process and Bernoulli distribution are respectively used to model the random network-induced effects, i.e., the network-induced delay and data packet dropouts. 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.