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Event-triggered integral reinforcement learning for nonlinear continuous-time systems
Qichao Zhang1,2; Dongbin Zhao
Conference NameIEEE Symposium Series on Computational Intelligence (SSCI)
Conference DateNov. 27 to Dec 1, 2017
Conference PlaceHonolulu, Hawaii, USA

In this paper, the optimal control problem for the continuous-time nonlinear systems with partially unknown dynamics is investigated. The event-triggered internal reinforcement learning (IRL) is proposed to approach the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Note that the knowledge of internal dynamics is relaxed, and the event-triggered control scheme is adopted to reduce the computational burden and communication resources. For the online implementation purpose, a single-critic neural network (NN) structure is constructed to approach the optimal value function and the optimal policy with convergence analysis. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed algorithm.

MOST Discipline Catalogue工学
Indexed ByEI
Document Type会议论文
Affiliation1.Institute of Automation, CAS
2.University of Chinese Academy of Sciences, CAS
Recommended Citation
GB/T 7714
Qichao Zhang,Dongbin Zhao. Event-triggered integral reinforcement learning for nonlinear continuous-time systems[C],2017.
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