File(s) not publicly available
An adaptive tracking controller using neural networks for a class of nonlinear systems
journal contributionposted 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. Â© 1998 IEEE.
Publication titleIEEE Transactions on neural networks
Department/SchoolSchool of Engineering
PublisherIEEE Neural Networks Council
Place of publicationUSA