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|Title:||A stable adaptive neural-network-based scheme for dynamical system control||Authors:||Xu, X.
|Issue Date:||22-Jul-2005||Citation:||Xu, X., Liang, Y.C., Lee, H.P., Lin, W.Z., Lim, S.P., Shi, X.H. (2005-07-22). A stable adaptive neural-network-based scheme for dynamical system control. Journal of Sound and Vibration 285 (3) : 653-667. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jsv.2004.08.034||Abstract:||A stable adaptive neural-network-based control scheme for dynamical systems is presented and a continuous recurrent neural network model of dynamical systems is constructed in this paper. A novel algorithm for updating weights in the neural network, which is not derived from the conventional back propagation algorithm, is also constructed. The proposed control law is obtained adaptively by a continuous recurrent neural network identifier, but not by a conventional neural network controller. In such a way, the stability in the sense of the Lyapunov stability can be guaranteed theoretically. The control error converges to a range near the zero point and remains within the domain throughout the course of the execution. Numerical experiments for a longitudinal vibration ultrasonic motor show that the proposed control scheme has good control performance. © 2004 Elsevier Ltd. All rights reserved.||Source Title:||Journal of Sound and Vibration||URI:||http://scholarbank.nus.edu.sg/handle/10635/59273||ISSN:||0022460X||DOI:||10.1016/j.jsv.2004.08.034|
|Appears in Collections:||Staff Publications|
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