{"title":"Spectrum computation and optimization for controllability Gramian of networked Laplacian systems with limited control placement","authors":"Yuexin Cao , Yibei Li , Zhuo Zou , Xiaoming Hu","doi":"10.1016/j.sysconle.2024.105945","DOIUrl":null,"url":null,"abstract":"<div><div>This paper investigates the problem of placing a given number of controls to optimize energy efficiency for a family of linear dynamical systems, whose structure is induced by the Laplacian of a square-grid network. To quantify the performance of control combinations, several metrics have been proposed based on the spectrum of the controllability Gramian. But commonly used algorithms to compute the spectrum are usually time-consuming. In this paper, we first classify five anchor symmetries of the network systems. Then motivated by various advantages of symmetric control combinations, we provide a method to compute the eigenvalues and eigenvectors of their controllability Gramians more efficiently. Specifically, we show that they can be expressed by those of two lower-dimensional matrices. Furthermore, our method can be applied for non-symmetric cases to provide upper and lower bounds for the spectrum of the controllability Gramians. Finally, by employing the sum of eigenvalues, i.e., the trace of controllability Gramian, as the objective function, we provide a closed-form algorithm to the spectrum optimization problem with a given number of controls subject to system controllability.</div></div>","PeriodicalId":49450,"journal":{"name":"Systems & Control Letters","volume":"193 ","pages":"Article 105945"},"PeriodicalIF":2.1000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems & Control Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167691124002330","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper investigates the problem of placing a given number of controls to optimize energy efficiency for a family of linear dynamical systems, whose structure is induced by the Laplacian of a square-grid network. To quantify the performance of control combinations, several metrics have been proposed based on the spectrum of the controllability Gramian. But commonly used algorithms to compute the spectrum are usually time-consuming. In this paper, we first classify five anchor symmetries of the network systems. Then motivated by various advantages of symmetric control combinations, we provide a method to compute the eigenvalues and eigenvectors of their controllability Gramians more efficiently. Specifically, we show that they can be expressed by those of two lower-dimensional matrices. Furthermore, our method can be applied for non-symmetric cases to provide upper and lower bounds for the spectrum of the controllability Gramians. Finally, by employing the sum of eigenvalues, i.e., the trace of controllability Gramian, as the objective function, we provide a closed-form algorithm to the spectrum optimization problem with a given number of controls subject to system controllability.
期刊介绍:
Founded in 1981 by two of the pre-eminent control theorists, Roger Brockett and Jan Willems, Systems & Control Letters is one of the leading journals in the field of control theory. The aim of the journal is to allow dissemination of relatively concise but highly original contributions whose high initial quality enables a relatively rapid review process. All aspects of the fields of systems and control are covered, especially mathematically-oriented and theoretical papers that have a clear relevance to engineering, physical and biological sciences, and even economics. Application-oriented papers with sophisticated and rigorous mathematical elements are also welcome.