有向图上多智能体系统最优输出一致性的分布式模型预测控制

IF 4.8 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Automatica Pub Date : 2025-05-22 DOI:10.1016/j.automatica.2025.112381

Nan Bai , Qishao Wang , Zhisheng Duan , Changxin Liu

{"title":"有向图上多智能体系统最优输出一致性的分布式模型预测控制","authors":"Nan Bai , Qishao Wang , Zhisheng Duan , Changxin Liu","doi":"10.1016/j.automatica.2025.112381","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, a distributed model predictive control (MPC) scheme is established to solve the optimal output consensus problem of heterogeneous multi-agent systems over directed graphs. Within the framework of MPC, we take both the control input and the consistent output state as decision variables to formulate a constrained optimization problem. Inspired by the primal decomposition technique and the push-sum dual average method, a distributed algorithm is designed to address the optimization problem. The convergence analysis of the proposed algorithm is given, which shows the convergence properties related to the number of iterations. Then, considering the limited computational resources in practical applications, an improved MPC-based approach with premature termination is further developed. The closed-loop stability is analyzed under the suboptimal MPC framework, deriving appropriate terminal conditions to guarantee the asymptotic consensus of multi-agent systems. Finally, numerical simulations demonstrate the effectiveness of the theoretical results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"178 ","pages":"Article 112381"},"PeriodicalIF":4.8000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed model predictive control for optimal output consensus of multi-agent systems over directed graphs\",\"authors\":\"Nan Bai , Qishao Wang , Zhisheng Duan , Changxin Liu\",\"doi\":\"10.1016/j.automatica.2025.112381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this paper, a distributed model predictive control (MPC) scheme is established to solve the optimal output consensus problem of heterogeneous multi-agent systems over directed graphs. Within the framework of MPC, we take both the control input and the consistent output state as decision variables to formulate a constrained optimization problem. Inspired by the primal decomposition technique and the push-sum dual average method, a distributed algorithm is designed to address the optimization problem. The convergence analysis of the proposed algorithm is given, which shows the convergence properties related to the number of iterations. Then, considering the limited computational resources in practical applications, an improved MPC-based approach with premature termination is further developed. The closed-loop stability is analyzed under the suboptimal MPC framework, deriving appropriate terminal conditions to guarantee the asymptotic consensus of multi-agent systems. Finally, numerical simulations demonstrate the effectiveness of the theoretical results.</div></div>\",\"PeriodicalId\":55413,\"journal\":{\"name\":\"Automatica\",\"volume\":\"178 \",\"pages\":\"Article 112381\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automatica\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0005109825002754\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatica","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0005109825002754","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}

引用次数: 0

摘要

针对有向图上异构多智能体系统的最优输出一致性问题，提出了一种分布式模型预测控制（MPC）方案。在MPC框架下，以控制输入和一致输出状态作为决策变量，构造约束优化问题。受原始分解技术和推和对偶平均方法的启发，设计了一种分布式算法来解决优化问题。给出了算法的收敛性分析，证明了算法的收敛性与迭代次数有关。然后，考虑到实际应用中有限的计算资源，进一步开发了一种改进的基于mpc的提前终止方法。分析了次优MPC框架下的闭环稳定性，给出了保证多智能体系统渐近一致的适当终端条件。最后，通过数值仿真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。

查看原文本刊更多论文

Distributed model predictive control for optimal output consensus of multi-agent systems over directed graphs

In this paper, a distributed model predictive control (MPC) scheme is established to solve the optimal output consensus problem of heterogeneous multi-agent systems over directed graphs. Within the framework of MPC, we take both the control input and the consistent output state as decision variables to formulate a constrained optimization problem. Inspired by the primal decomposition technique and the push-sum dual average method, a distributed algorithm is designed to address the optimization problem. The convergence analysis of the proposed algorithm is given, which shows the convergence properties related to the number of iterations. Then, considering the limited computational resources in practical applications, an improved MPC-based approach with premature termination is further developed. The closed-loop stability is analyzed under the suboptimal MPC framework, deriving appropriate terminal conditions to guarantee the asymptotic consensus of multi-agent systems. Finally, numerical simulations demonstrate the effectiveness of the theoretical results.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Automatica 工程技术-工程：电子与电气

CiteScore

10.70

自引率

7.80%

发文量

617

审稿时长

5 months

期刊介绍： Automatica is a leading archival publication in the field of systems and control. The field encompasses today a broad set of areas and topics, and is thriving not only within itself but also in terms of its impact on other fields, such as communications, computers, biology, energy and economics. Since its inception in 1963, Automatica has kept abreast with the evolution of the field over the years, and has emerged as a leading publication driving the trends in the field. After being founded in 1963, Automatica became a journal of the International Federation of Automatic Control (IFAC) in 1969. It features a characteristic blend of theoretical and applied papers of archival, lasting value, reporting cutting edge research results by authors across the globe. It features articles in distinct categories, including regular, brief and survey papers, technical communiqués, correspondence items, as well as reviews on published books of interest to the readership. It occasionally publishes special issues on emerging new topics or established mature topics of interest to a broad audience. Automatica solicits original high-quality contributions in all the categories listed above, and in all areas of systems and control interpreted in a broad sense and evolving constantly. They may be submitted directly to a subject editor or to the Editor-in-Chief if not sure about the subject area. Editorial procedures in place assure careful, fair, and prompt handling of all submitted articles. Accepted papers appear in the journal in the shortest time feasible given production time constraints.