Please use this identifier to cite or link to this item:
DC FieldValue
dc.titleOn learning wavelet control for affine nonlinear systems
dc.contributor.authorXu, J.-X.
dc.contributor.authorYan, R.
dc.contributor.authorWang, W.
dc.identifier.citationXu, J.-X.,Yan, R.,Wang, W. (2007). On learning wavelet control for affine nonlinear systems. Proceedings of the American Control Conference : 1287-1292. ScholarBank@NUS Repository. <a href="" target="_blank"></a>
dc.description.abstractFunction Approximation has been proven to be an effective approach when dealing with nonlinear dynamics. Among numerous function approximation methods, wavelet network shows unique advantage in terms of its orthonormality and multi-layer resolution properties, which enable the on-line tuning or closed-loop tuning for the wavelet network structure. Using such a constructive wavelet network, an adaptive iterative learning control approach was proposed for finite interval tracking problems [1]. In this work, the adaptive learning control approach with wavelet approximation (denoted by learning wavelet control or LWC) is applied two general classes of plants affine-in-input. One class is with nonlinear unknown input coefficient, and the other class is in cascade form. With the help of Lyapunov method, the learning convergence properties of the adaptive learning control system can be analyzed while the wavelet network undergoes on-line structure adaptation. © 2007 IEEE.
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleProceedings of the American Control Conference
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.