摘要
In this paper, an integral reinforcement learning-based adaptive neural network (NN) tracking control is developed for the continuous-time (CT) nonlinear system with unknown control directions. The long-term performance index in the CT domain is prescribed. Critic and action NNs are designed to approximate the unavailable long-term performance index and the unknown dynamics, respectively. The reinforcement signal is explicitly embedded in the updated law of the action NN and then the estimated long-term performance index can be minimized. Rigorous theoretical analysis is provided to show that the closed-loop system is stabilized and all closed-loop signals are semiglobally uniformly ultimately bounded. Finally, to demonstrate the control performance, simulation results are provided to verify the tacking control performance of an autonomous underwater vehicle model.
源语言 | 英语 |
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文章编号 | 8660673 |
页(从-至) | 4068-4077 |
页数 | 10 |
期刊 | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
卷 | 50 |
期 | 11 |
DOI | |
出版状态 | 已出版 - 11月 2020 |