Neural-network-based finite-time H∞ control for extended Markov jump nonlinear systems

This paper presents a neural-network-based finite-time H-infinity control design technique for a class of extended Markov jump nonlinear systems. The considered stochastic character is described by a Markov process, but with only partially known transition jump rates. The sufficient conditions for the existence of the desired controller are derived in terms of linear matrix inequalities such that the closed-loop system trajectory stays within a prescribed bound in a fixed time interval and has a guaranteed H-infinity noise attenuation performance for all admissible uncertainties and approximation errors of the neural networks. A numerical example is used to illustrate the effectiveness of the developed theoretic results. Copyright (C) 2009 John Wiley & Sons, Ltd.