An adaptive exact-penalty-based distributed resource allocation algorithm

被引：0

作者：

Shi X.-S. ^{[1
]}

Xu L. ^{[2
]}

Yang T. ^{[2
]}

机构：

[1] Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Jiangsu, Xuzhou

[2] The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning, Shenyang

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2022年 / 39卷 / 10期

关键词：

adaptive; distance function: non-smooth analysis; distributed resource allocation;

D O I：

10.7641/CTA.2022.10936

中图分类号：

学科分类号：

摘要：

Recently, the distributed resource allocation problem is one of the important issues in multi-agent systems. The distributed resource allocation problem aims to realize the optimal allocation of resources through the information interaction between agents. The local constraints of each agent bring great challenges to the algorithm design. First, an adaptive exact-penalty-based distributed resource allocation algorithm is proposed for the first-order multi-agent system, in which the local constraint is reformed by the distance function. Besides, the priori computation or knowledge of the global cost function is avoided based on the adaptive control scheme. Second, the above proposed first-order algorithm is modified for the second-order multi-agent system based on the tracking control technology. Then, by virtual of the nonsmooth analysis and convex function theory, the rigorous convergence analysis is given. Finally, the proposed algorithms are claimed effectively by the simulation examples. © 2022 South China University of Technology. All rights reserved.

引用

页码：1937 / 1945

页数：8

共 46 条

[31]

ZHU Y, WEN G, YU W, Et al., Nonsmooth resource allocation of multi-agent systems with disturbances: A proximal approach, IEEE Transactions on Control of Network Systems, 8, 3, pp. 1454-1464, (2021)

[32]

CHEN G, LI Z., Distributed optimal resource allocation over strongly connected digraphs: A surplus-based approach, Automatica, (2021)

[33]

ZHU Y, WEN G, YU W, Et al., Continuous-time distributed proximal gradient algorithms for nonsmooth resource allocation over general digraphs, IEEE Transactions on Networks Scinece and Engineering, 8, 2, pp. 1733-1744, (2021)

[34]

CHEN G, YANG Q, SONG Y, Et al., A distributed continuous-time algorithm for nonsmooth constrained optimization, IEEE Transactions on Automatic Control, 65, 11, pp. 4914-4921, (2020)

[35]

BAI L, YE M, SUN C, Et al., Distributed economic dispatch control via saddle point dynamics and consensus algorithms, IEEE Transactions on Control Systems Technology, 27, 2, pp. 898-905, (2019)

[36]

LI D, LI N, LEWIS F., Projection-free distributed optimization with nonconvex local objective functions and resource allocation constraint, IEEE Transactions on Control of Network Systems, 8, 1, pp. 413-422, (2021)

[37]

SHI Xiasheng, YANG Tao, LIN Zhiyun, Et al., Distributed resource allocation algorithm for second-order multi-agent systems in continuous-time, Acta Automatica Sinica, 47, 8, pp. 2050-2060, (2021)

[38]

CHERUKURI A, CORTES J., Initialization-free distributed coordination for economic dispatch under varying loads and generator commitment, Automatica, 74, pp. 183-193, (2016)

[39]

WANG D, WANG Z, WEN C, Et al., Second-order continuous-time algorithm for optimal resource allocation in power systems, IEEE Transactions on Industrial Informatics, 15, 2, pp. 626-637, (2019)

[40]

CHEN G, GUO Z., Initialization-free distributed fixed-time convergent algorithms for optimal resource allocation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 2, pp. 845-854, (2022)

← 1 2 3 4 5 →