Please use this identifier to cite or link to this item: https://doi.org/10.1049/ip-cta:19952122
Title: Direct neural control system: Nonlinear extension of adaptive control
Authors: Yuan, M. 
Poo, A.N. 
Hong, G.S. 
Issue Date: Nov-1995
Citation: Yuan, M., Poo, A.N., Hong, G.S. (1995-11). Direct neural control system: Nonlinear extension of adaptive control. IEE Proceedings: Control Theory and Applications 142 (6) : 661-667. ScholarBank@NUS Repository. https://doi.org/10.1049/ip-cta:19952122
Abstract: The methodology of design of a conventional model-reference-adaptive-control system is extended to design a direct neural control for a class of nonlinear system with structural uncertainty. A structured feedforward neural network, a Sigmoid-linear network, is used as the controller, which can be interpreted as a nonlinear extension of the conventional adaptive control. Without a specific pretraining stage, the weights of the neural network are adjusted online to minimize the error between the plant output and the desired output signal, according to a learning law derived in light of gradient-descent method. The local stability can be achieved provided that proper conditions are satisfied for the system. Simulation studies are carried out for linear and nonlinear plants, respectively, and verify the applicability of the proposed control strategy.
Source Title: IEE Proceedings: Control Theory and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/58113
ISSN: 13502379
DOI: 10.1049/ip-cta:19952122
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