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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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 > Intelligent Systems Research Centre|
Computer Science Research Institute
|Deposited By:||Dr Laxmidhar Behera|
|Deposited On:||13 Feb 2012 13:58|
|Last Modified:||13 Feb 2012 13:58|
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