Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72897
Title: Robust model reference adaptive control of robots based on neural network parametrization
Authors: Ge, S.S. 
Lee, T.H. 
Issue Date: 1997
Source: Ge, S.S.,Lee, T.H. (1997). Robust model reference adaptive control of robots based on neural network parametrization. Proceedings of the American Control Conference 3 : 2006-2010. ScholarBank@NUS Repository.
Abstract: In this paper, a robust model reference adaptive controller is presented for robots based on neural network parametrization. The controller is based on applying direct adaptive techniques to a basic fixed controller for better control performance, while a sliding mode control is introduced to guarantee robust closed-loop stability. It is shown that if Bounded Basis Function (BBF) networks are used for the parallel NN, uniformly stable adaptation is assured and asymptotic tracking of the reference signal is achieved.
Source Title: Proceedings of the American Control Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/72897
ISSN: 07431619
Appears in Collections:Staff Publications

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