Integral Reinforcement Learning-Based Adaptive NN Control for Continuous-Time Nonlinear MIMO Systems with Unknown Control Directions

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72 引用 (Scopus)

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
文章编号8660673
页(从-至)4068-4077
页数10
期刊IEEE Transactions on Systems, Man, and Cybernetics: Systems
50
11
DOI
出版状态已出版 - 11月 2020

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