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Volume 10 Issue 12
Dec.  2023

IEEE/CAA Journal of Automatica Sinica

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D. D. Yue, S. Baldi, J. D. Cao, Q. Li, and  B. De Schutter,  “Distributed adaptive resource allocation: An uncertain saddle-point dynamics viewpoint,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 12, pp. 2209–2221, Dec. 2023. doi: 10.1109/JAS.2023.123402
Citation: D. D. Yue, S. Baldi, J. D. Cao, Q. Li, and  B. De Schutter,  “Distributed adaptive resource allocation: An uncertain saddle-point dynamics viewpoint,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 12, pp. 2209–2221, Dec. 2023. doi: 10.1109/JAS.2023.123402

Distributed Adaptive Resource Allocation: An Uncertain Saddle-Point Dynamics Viewpoint

doi: 10.1109/JAS.2023.123402
Funds:  This work was supported in part by the China Postdoctoral Science Foundation (BX2021064), the Fundamental Research Funds for the Central Universities (2242022R20030), the National Key R&D Program of China (2022YFE0198700), and the Natural Science Foundation of China (62150610499, 62073074, 61833005)
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  • This paper addresses distributed adaptive optimal resource allocation problems over weight-balanced digraphs. By leveraging state-of-the-art adaptive coupling designs for multiagent systems, two adaptive algorithms are proposed, namely a directed-spanning-tree-based algorithm and a node-based algorithm. The benefits of these algorithms are that they require neither sufficiently small or unitary step sizes, nor global knowledge of Laplacian eigenvalues, which are widely required in the literature. It is shown that both algorithms belong to a class of uncertain saddle-point dynamics, which can be tackled by repeatedly adopting the Peter-Paul inequality in the framework of Lyapunov theory. Thanks to this new viewpoint, global asymptotic convergence of both algorithms can be proven in a unified way. The effectiveness of the proposed algorithms is validated through numerical simulations and case studies in IEEE 30-bus and 118-bus power systems.

     

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    Highlights

    • Distributed adaptive resource allocation problems over digraphs are addressed. An uncertain saddle-point dynamics viewpoint is provided
    • Two novel adaptive algorithms are proposed: directed-spanning-tree-based and node-based
    • Neither sufficiently small or unitary step sizes, nor global knowledge of Laplacian eigenvalues, are needed
    • Global asymptotic convergence of both algorithms are proven in a unified way, and are testified via simulations and IEEE case studies

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