Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCIS.2013.6751580
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dc.titleAdaptive optimal control for linear discrete time-varying systems
dc.contributor.authorGe, S.S.
dc.contributor.authorWang, C.
dc.contributor.authorLi, Y.
dc.contributor.authorLee, T.H.
dc.contributor.authorAng Jr., M.H.
dc.date.accessioned2014-10-07T04:41:15Z
dc.date.available2014-10-07T04:41:15Z
dc.date.issued2013
dc.identifier.citationGe, S.S.,Wang, C.,Li, Y.,Lee, T.H.,Ang Jr., M.H. (2013). Adaptive optimal control for linear discrete time-varying systems. Proceedings of the 2013 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2013 : 66-71. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICCIS.2013.6751580" target="_blank">https://doi.org/10.1109/ICCIS.2013.6751580</a>
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83440
dc.description.abstractIn this paper, adaptive optimal control is proposed for linear discrete time-varying (LDTV) systems subject to unknown system dynamics. The idea of the method is a direct application of the Q-learning adaptive dynamic programming for time-varying systems. In order to derive the optimal control policy, an actor-critic structure is constructed and the time-varying least square method is adopted for parameter adaptation. The derived control policy robustly stabilizes the time-varying system and guarantees an optimal control performance. As no particular system information is required throughout the process, the proposed method provides a feasible solution to a large variety of applications. The validity of the proposed method is verified through simulation studies. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICCIS.2013.6751580
dc.sourceScopus
dc.subjectadaptive dynamic programming
dc.subjectadaptive optimal control
dc.subjectLDTV systems
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/ICCIS.2013.6751580
dc.description.sourcetitleProceedings of the 2013 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2013
dc.description.page66-71
dc.identifier.isiutNOT_IN_WOS
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