Please use this identifier to cite or link to this item: https://doi.org/10.1109/CDC.2007.4434177
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dc.titleOutput feedback NN control of NARMA systems using discrete nussbaum gain
dc.contributor.authorGe, S.S.
dc.contributor.authorYang, C.G.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-19T03:22:43Z
dc.date.available2014-06-19T03:22:43Z
dc.date.issued2007
dc.identifier.citationGe, S.S.,Yang, C.G.,Lee, T.H. (2007). Output feedback NN control of NARMA systems using discrete nussbaum gain. Proceedings of the IEEE Conference on Decision and Control : 4681-4686. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/CDC.2007.4434177" target="_blank">https://doi.org/10.1109/CDC.2007.4434177</a>
dc.identifier.isbn1424414989
dc.identifier.issn01912216
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71349
dc.description.abstractIn this paper, output feedback adaptive neural network (NN) control is investigated for a class of discrete-time NARMA (nonlinear-autoregressive- moving-average) system. To solve the noncausal problem in control design, the system is transformed by future outputs prediction. The difficulty of nonaffine appearance of the control input is overcome by exploiting of implicit function theorem. The lack of a priori knowledge on the control directions is solved by using discrete Nussbaum gain. The high-order-neural-network (HONN) is employed to approximate the unknown ideal control. The closed-loop system achieves semi-global-uniformly-ultimately-boundedness (SGUUB) stability and the output tracking error is made within a small neighborhood around zero by suitably choosing the design parameters. Simulation results are presented to demonstrate the effectiveness of the proposed control approach. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CDC.2007.4434177
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CDC.2007.4434177
dc.description.sourcetitleProceedings of the IEEE Conference on Decision and Control
dc.description.page4681-4686
dc.description.codenPCDCD
dc.identifier.isiutNOT_IN_WOS
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