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

journal contribution
posted on 2023-05-16, 11:27 authored by Man, Z, Wu, H, Palaniswami, M
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.

History

Publication title

IEEE Transactions on neural networks

Volume

9

Issue

5

Pagination

947-954

ISSN

1045-9227

Department/School

School of Engineering

Publisher

IEEE Neural Networks Council

Place of publication

USA

Repository Status

  • Restricted

Socio-economic Objectives

Integrated systems

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