UM
Adaptive neural network output-feedback control for a class of discrete-time nonlinear systems in presence of input saturation
Wang X.2; Li T.2; Chen C.L.P.2
2017
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10639 LNCS
Pages165-173
AbstractIn this paper, an adaptive neural network output-feedback control approach is presented for a class of discrete-time nonlinear strict-feedback systems in presence of input saturation. An auxiliary design system is employed to overcome the problem of input saturation constraint, and states of auxiliary design system are utilized to develop the tracking control. The high-order neural network (HONN) is employed to approximate unknown function. It is shown via Lyapunov theory that all the signals in closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the control parameters appropriately. A simulation example is included to illustrate the effectiveness of the proposed approach.
KeywordDiscrete-time nonlinear systems Input saturation constraint Neural network control Output-feedback control
DOI10.1007/978-3-319-70136-3_18
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Dalian Maritime University
Recommended Citation
GB/T 7714
Wang X.,Li T.,Chen C.L.P.. Adaptive neural network output-feedback control for a class of discrete-time nonlinear systems in presence of input saturation[C],2017:165-173.
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