Please use this identifier to cite or link to this item: https://doi.org/10.1109/CDC.2008.4738830
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dc.titleStable adaptive neural network control of MIMO nonaffine nonlinear discrete-time systems
dc.contributor.authorZhai, L.
dc.contributor.authorChai, T.
dc.contributor.authorYang, C.
dc.contributor.authorGe, S.S.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-19T03:28:35Z
dc.date.available2014-06-19T03:28:35Z
dc.date.issued2008
dc.identifier.citationZhai, L.,Chai, T.,Yang, C.,Ge, S.S.,Lee, T.H. (2008). Stable adaptive neural network control of MIMO nonaffine nonlinear discrete-time systems. Proceedings of the IEEE Conference on Decision and Control : 3646-3651. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/CDC.2008.4738830" target="_blank">https://doi.org/10.1109/CDC.2008.4738830</a>
dc.identifier.isbn9781424431243
dc.identifier.issn01912216
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71855
dc.description.abstractIn this paper, stable adaptive neural network (NN) control, a combination of weighted one-step-ahead control and adaptive NN is developed for a class of multi-input-multioutput (MIMO) nonaffine nonlinear discrete-time systems. The weighted one-step-ahead control is designed to stabilize the nominal linear system, while the adaptive NN compensator is introduced to deal with the nonlinearities. Under the assumption that the inverse control gain matrix has an either positive definite or negative definite symmetric part, the obstacle in NN weights tuning for the MIMO systems is transformed to unknown control direction problem for single-input-singleoutput (SISO) system. Discrete Nussbaum gain is introduced into the NN weights adaptation law to overcome the unknown control direction problem. It is proved that all signals of the closed-loop system are bounded, while the tracking error converges to a compact set. Simulation result illustrates the effectiveness of the proposed control. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CDC.2008.4738830
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CDC.2008.4738830
dc.description.sourcetitleProceedings of the IEEE Conference on Decision and Control
dc.description.page3646-3651
dc.description.codenPCDCD
dc.identifier.isiutNOT_IN_WOS
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