TY - JOUR
T1 - Observer-based adaptive neural optimal control for discrete-time systems in nonstrict-feedback form
AU - Zhao, Shiyi
AU - Liang, Hongjing
AU - Ahn, Choon Ki
AU - Du, Peihao
N1 - Funding Information:
This work was partially supported by the National Natural Science Foundation of China ( 61703051 ), and the Department of Education of Liaoning Province ( LZ2017001 ), and the Ph.D. Start-up Fund of Liaoning Province (20170520124), and partially supported by the National Research Foundation of Korea through the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325 .
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/7/20
Y1 - 2019/7/20
N2 - 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.
AB - 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.
KW - Adaptive near-optimal control
KW - Backstepping control
KW - Dead-zone input
KW - Nonstrict-feedback nonlinear system
UR - http://www.scopus.com/inward/record.url?scp=85064656796&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2019.03.029
DO - 10.1016/j.neucom.2019.03.029
M3 - Article
AN - SCOPUS:85064656796
SN - 0925-2312
VL - 350
SP - 170
EP - 180
JO - Neurocomputing
JF - Neurocomputing
ER -