Title
Event-Triggered Adaptive Dynamic Programming for Zero-Sum Game of Partially Unknown Continuous-Time Nonlinear Systems
Abstract
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.
Year
DOI
Venue
2020
10.1109/TSMC.2018.2852810
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Adaptive dynamic programming (ADP),event-triggered control,Hamilton–Jacobi–Isaacs (HJI) equation,neural network (NN) identifier,zero-sum (ZS) game
Journal
50
Issue
ISSN
Citations 
9
2168-2216
9
PageRank 
References 
Authors
0.43
9
3
Name
Order
Citations
PageRank
Shan Xue15111.69
Biao Luo255423.80
Derong Liu35457286.88