Distributed resource allocation via multi-agent systems under time-varying networks

Lu, Kaihong; Xu, Hang; Zheng, Yufan

doi:10.1016/j.automatica.2021.110059

Cited by 35 publications

(10 citation statements)

References 29 publications

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“…Remark Compared with the existing distributed resource allocation algorithms with explicit gradient information of cost functions, 9‐20 algorithm (6) is designed with the unknown cost functions. The estimation of gradient information, obtained by the extremum seeking control with the values of cost functions, replaces the explicit gradient information in the above‐mentioned algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…The estimation of gradient information, obtained by the extremum seeking control with the values of cost functions, replaces the explicit gradient information in the above-mentioned algorithms. Moreover, the above-mentioned distributed algorithms [9][10][11][12][13][14][15][16][17][18][19][20] are invalid in the application of multi-agent systems with input dead-zone.…”

Section: Lemma 4 (Theorem 1 In the Work Ofmentioning

confidence: 99%

“…In recent years, the distributed resource allocation problem for multi‐agent systems has attracted an increasing interest as it involves a variety of applications, such as communication networks, 1‐3 sensor networks 4,5 and smart grids 6‐8 . Based on the primal‐dual dynamics, numerous distributed resource allocation algorithms have been proposed for agents who know their own cost functions with the explicit expression 9‐20 …”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] Based on the primal-dual dynamics, numerous distributed resource allocation algorithms have been proposed for agents who know their own cost functions with the explicit expression. [9][10][11][12][13][14][15][16][17][18][19][20] However, in some practical engineering systems, cost functions may be unknown due to the existence of uncertain or unknown parameters, such as unknown position information or environment conditions in sensor networks, 21,22 uncertain traffic demands in the network routing 23 and the unknown price functions in energy markets. 6,24 To deal with the resource allocation problem with unknown cost functions, some results have been reported.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed extremum‐seeking based resource allocation algorithm with input dead‐zone

Cai

Zhong²,

Li³

et al. 2023

Intl J Robust & Nonlinear

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This paper studies distributed resource allocation problem for agents with input dead-zone, which is not considered in the existing work. At first, the primal problem is transformed to an auxiliary problem by using the exact penalty method to deal with local inequality constraints. It is assumed that the explicit expressions of cost functions and local inequality constraints are unknown to agents but the values of the cost and constraint functions can be obtained. Under such a setup, the extremum seeking control is used to estimate the gradient information. Thus, to obtain the optimal allocation, a novel distributed algorithm is designed by the virtue of the extremum seeking control and a dynamic compensating mechanism which is used to handle the effects of the input dead-zone. Due to a two time-scale structure of the designed distributed algorithm, the semi-globally practically asymptotical convergence of all agents' decisions to the optimal allocation is obtained by the singular perturbation technique. Finally, numerical examples of economic dispatch in smart grids are given to verify the effectiveness of our proposed method.

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Section: Resultsmentioning

confidence: 99%

Section: Lemma 4 (Theorem 1 In the Work Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed extremum‐seeking based resource allocation algorithm with input dead‐zone

Cai

Zhong²,

Li³

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…An approach to resolve the scalability issue is to use a distributed optimization algorithm for multi-agent systems, which has been gaining significant attention [20][21][22][23][24][25][26][27]. However, conventional distributed optimization methods require communication between service providers at each algorithm iteration.…”

Section: Related Workmentioning

confidence: 99%

Communication‐aware distributed rebalancing for cooperative car‐sharing service

Hayashi

Sakurama

2022

IET Control Theory & Appl

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This study presents a cooperative car‐sharing system in which various service providers jointly operate a sharing service. A distributed algorithm is proposed for rebalancing control to reduce deadhead vehicles as much as possible under the constraints of dispatchable vehicles and parking slots at car stations. Each provider cooperatively searches for the optimal solution by exchanging the estimated values of the optimal dual solutions over the communication networks between providers. Event‐driven communication is considered in which information exchange with the neighboring providers is performed only when the amount of change of the estimated optimal dual solution exceeds a threshold. Numerical instances explain that the estimations of all service providers meet the optimal rebalancing solutions.

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Distributed optimal resource allocation with second‐order multi‐Agent systems

Tian

Zhu

2023

Optim Control Appl Methods

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This paper studies the resource allocation problems with second‐order multi‐agent systems under the constraint of the global network resource. A novel distributed continuous‐time algorithm, which only uses the local information, is proposed. By using matrix transform and differential inequality technique, the exponential convergence is obtained under the undirected and connected graph. To reduce energy consumption and communication costs, a distributed event‐triggered algorithm is designed. It is proved that not only this algorithm converges exponentially to the optimal allocation, but also the event‐triggered time sequence does not exhibit Zeno‐behavior. Finally, numerical examples illustrate the effectiveness of theoretical results.

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Distributed resource allocation via multi-agent systems under time-varying networks

Cited by 35 publications

References 29 publications

Distributed extremum‐seeking based resource allocation algorithm with input dead‐zone

Distributed extremum‐seeking based resource allocation algorithm with input dead‐zone

Communication‐aware distributed rebalancing for cooperative car‐sharing service

Distributed optimal resource allocation with second‐order multi‐Agent systems

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