期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 49, 期 3, 页码 579-588出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2810117
关键词
Adaptive dynamic programming (ADP); approximate dynamic programming; neural network (NN); reinforcement learning (RL)
资金
- National Natural Science Foundation of China [61433004, 61627809, 61621004]
- IAPI Fundamental Research Funds [2013ZCX14]
This paper investigates the robust control issues of nonlinear multiplayer systems by utilizing adaptive dynamic programming (ADP) methods and fills a gap in the ADP field, where actuator uncertainties for multiplayer systems are still not addressed. Two types of actuator uncertainties including bounded nonlinear perturbation and unknown constant actuator fault are taken into consideration. First, a data-driven reinforcement learning (RL) approach is derived to learn the optimal solutions of multiplayer nonzero-sum games. Then, based on the obtained optimal control policies, two robust control schemes are developed to handle these two different types of uncertainties, respectively, and the associated stability analysis is also provided. To implement the proposed iterative RL approach, a single neural network (NN) architecture with least-square-based updating law is given, which reduces the computation burden compared with the traditional dual NN architecture. Finally, two numerical examples are shown to test the feasibility of our proposed schemes.
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