Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2010.2047115
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dc.titleAdaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function
dc.contributor.authorRen, B.
dc.contributor.authorGe, S.S.
dc.contributor.authorTee, K.P.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-17T02:36:55Z
dc.date.available2014-06-17T02:36:55Z
dc.date.issued2010-08
dc.identifier.citationRen, B., Ge, S.S., Tee, K.P., Lee, T.H. (2010-08). Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function. IEEE Transactions on Neural Networks 21 (8) : 1339-1345. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2010.2047115
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54910
dc.description.abstractIn this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach. © 2006 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TNN.2010.2047115
dc.sourceScopus
dc.subjectBarrier function
dc.subjectneural networks (NNs)
dc.subjectoutput feedback nonlinear systems
dc.subjectunknown functions
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TNN.2010.2047115
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume21
dc.description.issue8
dc.description.page1339-1345
dc.description.codenITNNE
dc.identifier.isiut000283422600012
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