Event-triggered optimal control for nonlinear stochastic systems via adaptive dynamic programming

For nonlinear Itô-type stochastic systems, the problem of event-triggered optimal control (ETOC) is studied in this paper, and the adaptive dynamic programming (ADP) approach is explored to implement it. The value function of the Hamilton–Jacobi–Bellman(HJB) equation is approximated by applying crit...

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Veröffentlicht in:Nonlinear dynamics Jg. 105; H. 1; S. 387 - 401
Hauptverfasser: Zhang, Guoping, Zhu, Quanxin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.07.2021
Springer Nature B.V
Schlagworte:
Adaptive systems
Artificial neural networks
Automotive Engineering
Classical Mechanics
Control
Cost function
Dynamic programming
Dynamical Systems
Engineering
Liapunov direct method
Mechanical Engineering
Nonlinear control
Nonlinear systems
Optimal control
Original Paper
Stochastic systems
Upper bounds
Vibration
Nonlinear Itô-type stochastic systems
Neural network
Event-triggered control
Optimal control
Adaptive dynamic programming (ADP)
Hamilton–Jacobi–Bellman (HJB) equation
ISSN:0924-090X, 1573-269X
Online-Zugang:Volltext
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Zusammenfassung:For nonlinear Itô-type stochastic systems, the problem of event-triggered optimal control (ETOC) is studied in this paper, and the adaptive dynamic programming (ADP) approach is explored to implement it. The value function of the Hamilton–Jacobi–Bellman(HJB) equation is approximated by applying critical neural network (CNN). Moreover, a new event-triggering scheme is proposed, which can be used to design ETOC directly via the solution of HJB equation. By utilizing the Lyapunov direct method, it can be proved that the ETOC based on ADP approach can ensure that the CNN weight errors and states of system are semi-globally uniformly ultimately bounded in probability. Furthermore, an upper bound is given on predetermined cost function. Specifically, there has been no published literature on the ETOC for nonlinear Itô-type stochastic systems via the ADP method. This work is the first attempt to fill the gap in this subject. Finally, the effectiveness of the proposed method is illustrated through two numerical examples.
Bibliographie:ObjectType-Article-1
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-06624-8