سال انتشار:

2020

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عنوان انگلیسی مقاله:

Internal reinforcement adaptive dynamic programming for optimal containment control of unknown continuous-time multi-agent systems

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ترجمه فارسی عنوان مقاله:

تقویت داخلی برنامه نویسی پویا تطبیقی برای کنترل بهینه مهار سیستم های ناشناخته چند عامل با زمان پیوسته

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منبع:

Sciencedirect - Elsevier - Neurocomputing, 413 (2020) 85-95. doi:10.1016/j.neucom.2020.06.106

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نویسنده:

Jiefu Zhang a, Zhinan Peng a, Jiangping Hua,⇑, Yiyi Zhao b, Rui Luo a, Bijoy Kumar Ghosh a,c

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چکیده انگلیسی:

In this paper, a novel control scheme is developed to solve an optimal containment control problem of unknown continuous-time multi-agent systems. Different from traditional adaptive dynamic programming (ADP) algorithms, this paper proposes an internal reinforcement ADP algorithm (IR-ADP), in which the internal reinforcement signals are added in order to facilitate the learning process. Then a distributed containment control law is designed for each agent with the internal reinforcement signal. The convergence of this IR-ADP algorithm and the stability of the closed-loop multi-agent system are analyzed theoretically. For the implementation of the optimal controllers, three neural networks (NNs), namely internal reinforcement NNs, critic NNs and actor NNs, are utilized to approximate the internal reinforcement signals, the performance indices and optimal control laws, respectively. Finally, some simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
Keywords: Optimal containment control | Multi-agent system | Internal reinforcement learning | Adaptive dynamic programming | Neural network

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قیمت: رایگان

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توضیحات اضافی:

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