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

Dongdong Yue; Simone Baldi; Jinde Cao; Qi Li; Bart De Schutter

doi:10.1109/JAS.2023.123402

Volume 10 Issue 12

Dec. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(12): 2209-2221

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

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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

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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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Author Bio:
Dongdong Yue received the B.S. degree in applied mathematics from Hefei University of Technology in 2015, and the Ph.D. degree in control science and engineering from Southeast University in 2021. From May to August 2019, he visited the Control and Robotics Laboratory, Okayama Prefectural University, Japan. From October 2019 to October 2020, he visited the Delft Center for system and control, Delft University of Technology, The Netherlands. Currently, he is a Postdoc with the School of Mathematics, Southeast University. His research interests include adaptive control and distributed optimization. He received the Best Paper in Theory Award from the 18th IEEE International Conference on Networking, Sensing, and Control

Simone Baldi (Senior Member, IEEE) received the B.Sc. degree in electrical engineering, and the M.Sc. and Ph.D. degrees in automatic control systems engineering from the University of Florence, Italy, in 2005, 2007, and 2011, respectively. He is currently Professor at the School of Mathematics, Southeast University, with a guest position at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands, where he was Assistant Professor. Previously, he held postdoctoral researcher positions at the University of Cyprus, Cyprus and at the Information Technologies Institute, Centre for Research and Technology Hellas, Greece. His research interests include adaptive and learning systems with applications in networked control systems, smart energy and intelligent vehicle systems

Jinde Cao (Fellow, IEEE) received the B.S. degree from Anhui Normal University, the M.S. degree from Yunnan University, and the Ph.D. degree from Sichuan University, all in mathematics/applied mathematics, in 1986, 1989, and 1998, respectively. He was a Postdoctoral Research Fellow at the Department of Automation and Computer-Aided Engineering, Chinese University of Hong Kong, China, from 2001 to 2002. He is an Endowed Chair Professor, the Dean of the School of Mathematics and the Director of the Research Center for Complex Systems and Network Sciences at Southeast University (SEU). He is also the Director of the National Center for Applied Mathematics at SEU-Jiangsu of China and the Director of the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence of China. His research interests include neural network modelling and analysis.Professor Cao was a recipient of the National Innovation Award of China, Obada Prize and the Highly Cited Researcher Award in Engineering, Computer Science, and Mathematics by Clarivate Analytics. He is elected as a Member of Russian Academy of Sciences, a Member of the Academy of Europe, a Member of Russian Academy of Engineering, a Member of the European Academy of Sciences and Arts, a Member of the Lithuanian Academy of Sciences, a Fellow of African Academy of Sciences, and a Fellow of Pakistan Academy of Sciences

Qi Li received the B.Eng., M.Eng. and Ph.D. degrees from Southeast University in 1983, 1986, and 1992, respectively, all in automatic control. He was a Visiting Scholar with the Massachusetts Institute of Technology, USA, from 2003 to 2004, involved in the project of Alpha Magnetic Spectrometer, which was sent to the International Space Station later to detect the antisubstance. He has been a Full Professor with School of Automation, Southeast University, since 1999. His current research interests include intelligent control, optimal control of complex industrial process, and integration of management and control

Bart De Schutter (Fellow, IEEE) received the Ph.D. degree in applied sciences (summa cum laude with congratulations of the examination jury) from K.U. Leuven, Belgium in 1996. Currently, he is a Full Professor and Head of Department at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. His current research interests include multi-level and distributed control, control of discrete-event and hybrid systems, intelligent transportation systems and smart infrastructure networks.He is Senior Editor of the IEEE Transactions on Intelligent Transportation Systems and Associate Editor of IEEE Transactions on Automatic Control
Corresponding author: Dongdong Yue, e-mail: yued@seu.edu.cn; Jinde Cao, e-mail: jdcao@seu.edu.cn
Received Date: 2022-10-13
Revised Date: 2022-11-14
Accepted Date: 2022-12-20

Available Online: 2023-09-06

Abstract

Abstract

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.
- Adaptive systems,
- directed graphs,
- resource allocation,
- saddle-point dynamics

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References(45)

References

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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

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

doi: 10.1109/JAS.2023.123402

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通讯作者: 陈斌, bchen63@163.com

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