Distributed nonsmooth resource allocation algorithms over second-order multi-agent systems

被引：0
|
作者：
Shi X.-S. [1 ,2 ]
Sun J.-Y. [3 ]
Xu L. [3 ]
Yang T. [3 ]
机构：
[1] School of Artificial Intelligence, Anhui University, Hefei
[2] Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, Dalian University of Technology, Dalian
[3] The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 05期
关键词：
adaptive; initialization-free; nonsmooth; resource allocation; second-order systems;
D O I：
10.13195/j.kzyjc.2022.1262
中图分类号：
学科分类号：
摘要：
The distributed resource allocation problem aims to allocate a mount of resources under some local constraints while minimizing the total cost function. First, based on the Karush-Kuhn-Tucker conditions, an initialization-free distributed optimization algorithm is proposed for second-order multi-agent systems over an undirected connected network. The global equality constraint dual variable is developed with a proportional-integral control, and the local convex function inequality constraint dual variable is sought adaptively. Based on the set-value LaSalle’s invariance principle, it is shown that the designed algorithm asymptotically converges to the optimal point if the global cost function is nonsmooth convex. Then, the proposed algorithm is extended to Euler-Lagrange multi-agent systems over an undirected connected network. Furthermore, by virtual of the Barbalat’s lemma, it is shown that the proposed algorithm asymptotically converges to the optimal solution if the global cost function is nonsmooth convex. Finally, several numerical examples are used to illustrate the performance of the proposed algorithms. © 2023 Northeast University. All rights reserved.
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页码：1336 / 1344
页数：8
相关论文
共 53 条
  • [1] Ren M, Wang K B, Shen L C., Planning and simulation for multi-UAV cooperative penetration mission, Control and Decision, 26, 1, pp. 157-160, (2011)
  • [2] Yin Z, He W, Zou Y, Et al., Efficient fomation of flapping-wing aerial vehicles based on wild geese queue effect, Acta Automatica Sinica, 47, 6, pp. 1355-1367, (2021)
  • [3] Niu J J, Deng Z D, Li C., Distributed scheduling approaches in wireless sensor network, Acta Automatica Sinica, 37, 5, pp. 517-528, (2011)
  • [4] Teng F, Shan Q H, Li T S., Intelligent ship integrated energy system and its distributed optimal scheduling algorithm, Acta Automatica Sinica, 46, 9, pp. 1809-1817, (2020)
  • [5] Yang T, Chai T Y., Research status and prospects of distributed collaborative optimization, Scientia Sinica: Technologica, 50, 11, pp. 1414-1425, (2020)
  • [6] Yang T, Xu L, Yi X L, Et al., Event-triggered distributed optimization algorithms, Acta Automatica Sinica, 48, 1, pp. 133-143, (2022)
  • [7] Liang S, Zeng X L, Hong Y G., Distributed nonsmooth optimization with coupled inequality constraints via modified Lagrangian function, IEEE Transactions on Automatic Control, 63, 6, pp. 1753-1759, (2018)
  • [8] Yi X L, Yao L S, Yang T, Et al., Distributed optimization for second-order multi-agent systems with dynamic event-triggered communication, 2018 IEEE Conference on Decision and Control, pp. 3397-3402, (2019)
  • [9] Deng Z H., Distributed algorithm design for resource allocation problems of high-order multiagent systems, IEEE Transactions on Control of Network Systems, 8, 1, pp. 177-186, (2021)
  • [10] Li G P, Wang X Y, Li S H., Finite-time distributed approximate optimization algorithms of higher order multiagent systems via penalty-function-based method, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 10, pp. 6174-6182, (2022)
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