Distributed Optimization of Heterogeneous Linear Multi-Agent Systems With Unknown Disturbances and Optimal Gain Tuning

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-29 DOI:10.1109/TSIPN.2025.3574852

Mengmeng Duan;Shanying Zhu;Ziwen Yang;Cailian Chen;Xinping Guan

{"title":"Distributed Optimization of Heterogeneous Linear Multi-Agent Systems With Unknown Disturbances and Optimal Gain Tuning","authors":"Mengmeng Duan;Shanying Zhu;Ziwen Yang;Cailian Chen;Xinping Guan","doi":"10.1109/TSIPN.2025.3574852","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. To solve this problem, we propose a distributed controller design framework, which reduces the controller design for heterogeneous linear multi-agent systems to the stabilizer design for first-order multi-agent systems. By this framework, we propose two kinds of dynamic controllers under the strong convexity of the objective function and the restricted secant inequality condition, respectively. Based on the optimal condition and singular perturbation analysis technique, we prove that the system converges to the optimal state if the disturbances tend to be constant or vary slowly. To further optimize the performance criterion under system stability and input constraint, we provide an optimal gain tuning algorithm such that the system stability, optimality and feasibility are simultaneously achieved. Numerical examples are provided to illustrate the effectiveness of the theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"563-576"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11017692/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. To solve this problem, we propose a distributed controller design framework, which reduces the controller design for heterogeneous linear multi-agent systems to the stabilizer design for first-order multi-agent systems. By this framework, we propose two kinds of dynamic controllers under the strong convexity of the objective function and the restricted secant inequality condition, respectively. Based on the optimal condition and singular perturbation analysis technique, we prove that the system converges to the optimal state if the disturbances tend to be constant or vary slowly. To further optimize the performance criterion under system stability and input constraint, we provide an optimal gain tuning algorithm such that the system stability, optimality and feasibility are simultaneously achieved. Numerical examples are provided to illustrate the effectiveness of the theoretical results.

查看原文本刊更多论文

具有未知扰动的异构线性多智能体系统的分布优化与最优增益调谐

本文研究了具有未知扰动的异构线性多智能体系统的分布式优化问题。为了解决这一问题，我们提出了一种分布式控制器设计框架，将异构线性多智能体系统的控制器设计简化为一阶多智能体系统的稳定器设计。在此框架下，我们分别在目标函数的强凸性和限制割线不等式条件下提出了两种动态控制器。基于最优条件和奇异摄动分析技术，证明了当扰动趋于恒定或变化缓慢时，系统收敛于最优状态。为了进一步优化系统稳定性和输入约束下的性能准则，我们提出了一种同时实现系统稳定性、最优性和可行性的最优增益调谐算法。数值算例验证了理论结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.