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CLC number: TP391.4

On-line Access: 2024-02-23

Received: 2023-08-22

Revision Accepted: 2024-02-23

Crosschecked: 2023-10-17

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Citations:  Bibtex RefMan EndNote GB/T7714


Shuai LIU


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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.2 P.214-224


Event-triggered distributed optimization formodel-free multi-agent systems

Author(s):  Shanshan ZHENG, Shuai LIU, Licheng WANG

Affiliation(s):  College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China; more

Corresponding email(s):   zhengss0904@163.com, liushuai871030@163.com, wanglicheng1217@163.com

Key Words:  Distributed optimization, Multi-agent systems, Model-free adaptive control, Event-triggered mechanism

Shanshan ZHENG, Shuai LIU, Licheng WANG. Event-triggered distributed optimization formodel-free multi-agent systems[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(2): 214-224.

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%T Event-triggered distributed optimization formodel-free multi-agent systems
%A Shanshan ZHENG
%A Shuai LIU
%A Licheng WANG
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300568

T1 - Event-triggered distributed optimization formodel-free multi-agent systems
A1 - Shanshan ZHENG
A1 - Shuai LIU
A1 - Licheng WANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
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EP - 224
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300568

In this paper, the distributed optimization problem is investigated for a class of general nonlinear modelfree multi-agent systems. The dynamical model of each agent is unknown and only the input/output data are available. A model-free adaptive control method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus scheme is developed to guarantee that the consensus error of the outputs of all agents is convergent. Then, by means of the distributed gradient descent method, a novel event-triggered model-free adaptive distributed optimization algorithm is put forward. Sufficient conditions are established to ensure the consensus and optimality of the addressed system. Finally, simulation results are provided to validate the effectiveness of the proposed approach.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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