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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

Cited: 0

Clicked: 410

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Shuai LIU

https://orcid.org/0000-0003-0523-022X

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

http://doi.org/10.1631/FITEE.2300568


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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Abstract: 
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.

事件触发机制下无模型多智能体系统的分布式优化

郑姗姗1,刘帅1,王立成2
1上海理工大学理学院,中国上海市,200093
2上海电力大学自动化工程学院,中国上海市,200090
摘要:研究了一类一般非线性无模型多智能体系统的分布式优化问题。每个智能体的动态模型是未知的,只能获得输入和输出数据的信息。首先,通过采用无模型自适应控制方法,将原来未知的非线性系统等效转化为动态线性化模型。然后,为保证所有智能体输出的一致性误差收敛,提出一种基于事件触发机制的一致性控制方案。其次,引入分布式梯度下降法,提出一种新的事件触发无模型自适应分布式优化算法。根据李亚普诺夫稳定性理论,给出闭环系统达到一致性和最优性的充分条件。最后,通过仿真实验验证算法设计方案的有效性。

关键词:分布式优化;多智能体系统;无模型自适应控制;事件触发机制

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

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