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dc.contributor.authorZhou, Jialingen_US
dc.contributor.authorLv, Yuezuen_US
dc.contributor.authorWen, Changyunen_US
dc.contributor.authorWen, Guanghuien_US
dc.identifier.citationZhou, J., Lv, Y., Wen, C. & Wen, G. (2022). Solving specified-time distributed optimization problem via sampled-data-based algorithm. IEEE Transactions On Network Science and Engineering, 9(4), 2747-2758.
dc.description.abstractDespite significant advances on distributed continuous-time optimization of multi-agent networks, there is still lack of an efficient algorithm to achieve the goal of distributed optimization at a pre-specified time, especially for the case with unbalanced directed topologies. Herein, a new out-degree based design structure is proposed for connected agents with directed topologies to collectively minimize the sum of individual objective functions and keep satisfying an equality constraint. With the designed algorithm, the settling time of distributed optimization can be exactly predefined. The specified selection of such a settling time is independent of not only the initial conditions of agents, but also the algorithm parameters and the communication topologies. Furthermore, the proposed algorithm can realize specified-time optimization by exchanging information among neighbors only at discrete sampling instants and thus reduces the communication burden. In addition, the equality constraint is always satisfied during the whole process, which makes the proposed algorithm applicable to online solving distributed optimization problems such as energy resource allocation. For the special case of undirected communication topologies, a reduced-order algorithm is also designed. Finally, the effectiveness of the theoretical analysis is justified by numerical simulations.en_US
dc.relation.ispartofIEEE Transactions on Network Science and Engineeringen_US
dc.rights© 2022 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleSolving specified-time distributed optimization problem via sampled-data-based algorithmen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.subject.keywordsDirected Graphen_US
dc.subject.keywordsDistributed Resource Allocationen_US
dc.description.acknowledgementThis work was supported in part by the National Natural Science Foundation of China under Grants 62003167, 61903083, 62073079, and 62088101, in part by the Fellowship of China Postdoctoral Science Foundation under Grant 2021TQ0039, and in part by the Beijing Institute of Technology Research Fund Program for Young Scholars.en_US
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