Input-Output Data-Based Output Antisynchronization Control of Multiagent Systems Using Reinforcement Learning Approach
This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controlle...
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| Vydané v: | IEEE transactions on industrial informatics Ročník 17; číslo 11; s. 7359 - 7367 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Piscataway
IEEE
01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1551-3203, 1941-0050 |
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| Abstract | This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy. |
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| AbstractList | This article investigates an output antisynchronization problem of multiagent systems by using an input–output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor–critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy. |
| Author | Hu, Jiangping Luo, Rui Ghosh, Bijoy Kumar Peng, Zhinan Zhao, Yiyi Nguang, Sing Kiong |
| Author_xml | – sequence: 1 givenname: Zhinan surname: Peng fullname: Peng, Zhinan email: zhinanpeng@126.com organization: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Yiyi surname: Zhao fullname: Zhao, Yiyi email: zhaoyy@swufe.edu.cn organization: School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China – sequence: 3 givenname: Jiangping orcidid: 0000-0002-7559-8604 surname: Hu fullname: Hu, Jiangping email: hjp_lzu@163.com organization: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Rui surname: Luo fullname: Luo, Rui email: nicole9922@163.com organization: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 5 givenname: Bijoy Kumar orcidid: 0000-0001-8123-6549 surname: Ghosh fullname: Ghosh, Bijoy Kumar email: bijoy.ghosh@ttu.edu organization: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 6 givenname: Sing Kiong orcidid: 0000-0003-4527-0082 surname: Nguang fullname: Nguang, Sing Kiong email: sk.nguang@auckland.ac.nz organization: Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland, New Zealand |
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| SubjectTerms | Artificial neural networks Control systems design Control theory Heuristic algorithms Incremental actor–critic (AC) network Informatics input–output data Iterative algorithms Iterative methods Learning Multi-agent systems Multiagent systems Network topology optimal antisynchronization Optimal control partially observable multiagent systems reinforcement learning (RL) Synchronization System dynamics |
| Title | Input-Output Data-Based Output Antisynchronization Control of Multiagent Systems Using Reinforcement Learning Approach |
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