首页|Adaptive Optimal Output Regulation of Intercon-nected Singularly Perturbed Systems With Application to Power Systems

Adaptive Optimal Output Regulation of Intercon-nected Singularly Perturbed Systems With Application to Power Systems

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This article studies the adaptive optimal output reg-ulation problem for a class of interconnected singularly per-turbed systems(SPSs)with unknown dynamics based on rein-forcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast time-scale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentral-ized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the sta-bility and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

Adaptive optimal controldecentralized controlout-put regulationreinforcement learning(RL)singularly perturbed systems(SPSs)

Jianguo Zhao、Chunyu Yang、Weinan Gao、Linna Zhou、Xiaomin Liu

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Engineering Research Center of Intelligent Control for Underground Space,Ministry of Education,China University of Mining and Technology,Xuzhou 221116

School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China

State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110819,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Jiangsu Province

6207332762273350BK20221112

2024

10.1109/JAS.2023.123651

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(3)
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