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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Qing Zhang,Zhikun Gong,Zhengquan Yang,Zengqiang Chen.Distributed Optimization for Heterogenous Second-Order Multi-Agent Systems[J].Journal of Harbin Institute Of Technology(New Series),2020,27(4):53-59.DOI:10.11916/j.issn.1005-9113.18124.
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Distributed Optimization for Heterogenous Second-Order Multi-Agent Systems
Author NameAffiliation
Qing Zhang College of Science, Civil Aviation University of China, Tianjin 300300, China 
Zhikun Gong College of Science, Civil Aviation University of China, Tianjin 300300, China 
Zhengquan Yang College of Science, Civil Aviation University of China, Tianjin 300300, China 
Zengqiang Chen College of Science, Civil Aviation University of China, Tianjin 300300, China
Department of Automation, Nankai University, Tianjin 300071, China 
Abstract:
A continuous-time distributed optimization was researched for second-order heterogeneous multi-agent systems. The aim of this study is to keep the velocities of all agents the same and make the velocities converge to the optimal value to minimize the sum of local cost functions. First, an effective distributed controller which only uses local information was designed. Then, the stability and optimization of the systems were verified. Finally, a simulation case was used to illustrate the analytical results.
Key words:  distributed optimization  heterogeneous multi-agent system  local cost function  consensus
DOI:10.11916/j.issn.1005-9113.18124
Clc Number:TP18
Fund:
Descriptions in Chinese:
  

二阶异质多智能体系统的分布式优化群集

张青1,弓志坤1,杨正全1,陈增强21

(1. 中国民航大学 理学院,天津 300300;

2. 南开大学 自动化学院 天津 300071)

创新点说明:

本文考虑将多智能体群集与分布式优化结合,通过算法推导、分析证明智能体在实现一致的同时能最小化总目标函数,实现优化。

研究目的:

考虑到在实际应用中,越来越多的场合需要不同类型的智能体进行协调工作, 即异质的智能体,而且在系统的规模大到一定程度时,需要对系统进行拆分,有 利于系统功能的扩展。因此,本文考虑异质多智能体系统的分布式优化。

研究方法

1)确定二阶异质多智能体的动力学模型;

2)设计分布式优化控制器;

3)利用非光滑分析以及李雅普诺夫稳定性证明多智能体系统达到稳定状态;

4)证明智能体的速度可以达到最优并且使得总目标函数最小。

研究结果:

1) 得到当 时,智能体的速度变得相同,即 ,从而 ,即智能体的位置距离保持不变。

2)存在全局变量 满足

,

结 论:

与已有的论文相比,本文设计的控制法则可以保证每个智能体的状态值达到 一致,而且在运动过程中,智能体的速度可以收敛到最优解附近。

关键词:分布式优化;异质多智能体;局部代价函数;一致

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