Robust asymptotic neuro-observer with time delay term

Abstract

This paper concerns the designing of the robust asymptotic state observer for the class of unknown nonlinear systems. The Luenberger-type observer is suggested to be extended in two ways: first, the unknown nonlinear dynamics is estimated by a dynamic neural network; second, the time delay term is added to compensate the arising differential effects in the Luenberger observer. The Lyapunov–Krasovskii technique is used to prove the robust asymptotic stability ‘on average’ of the neuro observer as well as the boundness of the observation error. Two examples dealing with the Van Der Pol oscillations and the single-link robot rotation are reported to demonstrate numerically the effectiveness of the suggested approach. Copyright © 2000 John Wiley & Sons, Ltd.