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Observer-based adaptive neural optimal control for discrete-time systems in nonstrict-feedback form

Authors
Zhao, ShiyiLiang, HongjingAhn, Choon KiDu, Peihao
Issue Date
20-7월-2019
Publisher
ELSEVIER
Keywords
Adaptive near-optimal control; Backstepping control; Nonstrict-feedback nonlinear system; Dead-zone input
Citation
NEUROCOMPUTING, v.350, pp.170 - 180
Indexed
SCIE
SCOPUS
Journal Title
NEUROCOMPUTING
Volume
350
Start Page
170
End Page
180
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/64082
DOI
10.1016/j.neucom.2019.03.029
ISSN
0925-2312
Abstract
This paper considers the problem of observer-based adaptive near-optimal control for a class of nonstrict-feedback discrete-time nonlinear systems with non-symmetric dead zone. In order to compensate the effect of dead-zone on the control performance, an adaptive auxiliary signal is constructed to estimate the unknown dead-zone parameters. For the unknown nonlinear functions, neural networks (NNs) are introduced to identify them and to estimate the unknown parameters. To resolve the difficulty resulting from the unavailable state variables, an NN-based observer is designed. Moreover, according to the framework of adaptive control, a novel reinforcement learning algorithm is developed to guarantee that the near-optimal control performance is achieved. Finally, some simulation results are provided to illustrate the effectiveness of the proposed control algorithm. (C) 2019 Elsevier B.V. All rights reserved.
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