期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 50, 期 9, 页码 3189-3199出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2852810
关键词
Adaptive dynamic programming (ADP); event-triggered control; Hamilton-Jacobi-Isaacs (HJI) equation; neural network (NN) identifier; zero-sum (ZS) game
资金
- National Natural Science Foundation of China [61503377, 61533017, U1501251]
In this paper, the zero-sum game problem is considered for partially unknown continuous-time nonlinear systems, and an event-triggered adaptive dynamic programming (ADP) method is developed to solve the problem. First, an identifier neural network (NN) and a critic NN are applied to approximate the drift system dynamics and the optimal value function, respectively. Subsequently, an event-triggered approach is developed based on ADP, which samples the states and updates the weights of NNs at the same time when the event-triggering condition is violated, such that the computational complexity is reduced. It is proved that the states and the error of NN weights are uniformly ultimately bounded. Finally, the effectiveness of the developed ADP-based event-triggered method is verified through simulation studies.
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