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Direct adaptive neural control for affine nonlinear systems

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Kar, Indrani and Behera, Laxmidhar (2009) Direct adaptive neural control for affine nonlinear systems. Applied Soft Computing, 9 . pp. 756-764. [Journal article]

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URL: http://www.sciencedirect.com/science/article/pii/S1568494608001567

DOI: 10.1016/j.asoc.2008.10.001

Abstract

This paper presents a direct adaptive neural control scheme for a class of affine nonlinear systems which are exactly input–output linearizable by nonlinear state feedback. For single-input–single-output (SISO) systems of the form View the MathML source, the control problem is comprehensively solved when both f(x) and g(x) are unknown. In this case, the control input comprises two terms. One is an adaptive feedback linearization term and the other one is a sliding mode term. The weight update laws for two neural networks, which approximate f(x) and g(x), have been derived to make the closed loop system Lyapunov stable. It is also shown that a similar control approach can be applied for a class of multi-input–multi-output (MIMO) systems whose structure is formulated in this paper. Simulation results for both SISO- and MIMO-type nonlinear systems have been presented to validate the theoretical formulations.

Item Type:Journal article
Keywords:Feedback linearization, Adaptive control, Neural network, Lyapunov stability
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:21059
Deposited By:Dr Laxmidhar Behera
Deposited On:13 Feb 2012 13:58
Last Modified:13 Feb 2012 13:58

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