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Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation
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Title: Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation
Authors: Wang, D; Mu, CX; Zhang, QC; Liu, DR
Author Full Names: Wang, Ding; Mu, Chaoxu; Zhang, Qichao; Liu, Derong
Source: NEUROCOMPUTING, 214 848-856; 10.1016/j.neucom.2016.07.002 NOV 19 2016
Language: English
Abstract: In this paper, the continuous-time input-constrained nonlinear H-infinity state feedback control under event based environment is investigated with adaptive critic designs and neural network implementation. The nonlinear H-infinity control issue is regarded as a two-player zero-sum game that requires solving the Hamilton-Jacobi-Isaacs equation and the adaptive critic learning (ACL) method is adopted toward the event-based constrained optimal regulation. The novelty lies in that the event-based design framework is combined with the ACL technique, thereby carrying out the input-constrained nonlinear H-infinity state feedback via adopting a non-quadratic utility function. The event-based optimal control law and the time-based worst-case disturbance law are derived approximately, by training an artificial neural network called a critic and eventually learning the optimal weight vector. Under the action of the event based state feedback controller, the closed-loop system is constructed with uniformly ultimately bounded stability analysis. Simulation studies are included to verify the theoretical results as well as to illustrate the event-based H-infinity control performance. (C) 2016 Elsevier B.V. All rights reserved.
ISSN: 0925-2312
eISSN: 1872-8286
IDS Number: EA6LS
Unique ID: WOS:000386741300080
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