期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 48, 期 5, 页码 1633-1646出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2017.2712617
关键词
Adaptive dynamic programming (ADP); globalized dual heuristic dynamic programming (GDHP); model-free; neural networks; zero-sum game
类别
资金
- National Science Foundation [ECCS 1053717, CMMI 1526835]
- National Natural Science Foundation of China [51529701]
- Beijing Natural Science Foundation [4162065]
In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with H-infinity optimal regulation control problem. By setting backward one step of the definition of performance index, the requirement of system dynamics, or an identifier is relaxed in the proposed method. Then, three neural networks are established to approximate the optimal saddle point feedback control law, the disturbance law, and the performance index, respectively. The explicit updating rules for these three neural networks are provided based on the data generated during the online learning along the system trajectories. The stability analysis in terms of the neural network approximation errors is discussed based on the Lyapunov approach. Finally, two simulation examples are provided to show the effectiveness of the proposed method.
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