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An adaptive tracking controller using neural networks for a class of nonlinear systems


Man, Z and Wu, H and Palaniswami, M, An adaptive tracking controller using neural networks for a class of nonlinear systems, IEEE Transactions on neural networks, 9, (5) pp. 947-954. ISSN 1045-9227 (1998) [Refereed Article]

DOI: doi:10.1109/72.712168


A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation example is performed in support of the proposed neural control scheme. © 1998 IEEE.

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Control engineering, mechatronics and robotics
Research Field:Field robotics
Objective Division:Manufacturing
Objective Group:Computer, electronic and communication equipment
Objective Field:Integrated systems
UTAS Author:Man, Z (Dr Zhihong Man)
ID Code:15250
Year Published:1998
Web of Science® Times Cited:112
Deposited By:Electrical Engineering
Deposited On:1998-08-01
Last Modified:2011-08-10

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