Title: A robust adaptive control using neural network

Authors: Hassen Mekki, Mohamed Chtourou, Nabil Derbel

Addresses: Research Unit on Intelligent Control, Design and Optimisation of Complex Systems (ICOS), National School of Engineers of Sfax, Sfax B.P.W 3038, Tunisia. ' Research Unit on Intelligent Control, Design and Optimisation of Complex Systems (ICOS), National School of Engineers of Sfax, Sfax B.P.W 3038, Tunisia. ' Research Unit on Intelligent Control, Design and Optimisation of Complex Systems (ICOS), National School of Engineers of Sfax, Sfax B.P.W 3038, Tunisia

Abstract: Feedback linearisation is an approach applied to non-linear control design and has attracted a great deal of research interest in recent years. The common assumptions are that the full state is measurable, and that the system is exactly linearly parameterised and feedback linearisable (input/state or input/output). With few exceptions, the robustness issue is not addressed. In practical implementation of exactly linearising control laws, the chief drawback is that they are based on exact cancellation of non-linear terms. If there is any uncertainty in the knowledge of the non-linear functions, the cancellation is not exact and the resulting input–output equation is not linear. The aim of this paper discusses the use of Neural Network (NN)-based adaptive control to get asymptotically exact cancellation.

Keywords: feedback linearisation; adaptive control; robustness; neural networks; NNs; robust control; nonlinear control design.

DOI: 10.1504/IJMIC.2007.014327

International Journal of Modelling, Identification and Control, 2007 Vol.2 No.1, pp.58 - 65

Published online: 01 Jul 2007 *

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