{"title":"An output sensitivity problem for a class of linear distributed systems with uncertain initial state","authors":"S. B. Rhila, M. Rachik, A. Tridane","doi":"10.24425/acs.2020.132589","DOIUrl":null,"url":null,"abstract":"In this paper, we consider an infinite dimensional linear systems. It is assumed that the initial state of system is not known throughout all the domain Ω (cid:26) R n , the initial state x 0 2 L 2 ( Ω ) is supposed known on one part of the domain Ω and uncertain on the rest. That means Ω = ! 1 [ ! 2 [ : : : [ ! t with ! i \\ ! j = ∅ , 8 i ; j 2 f 1 ; : : :; t g , i , j where ! i , ∅ and x 0 ( (cid:18) ) = (cid:11) i for (cid:18) 2 ! i , 8 i , i.e., x 0 ( (cid:18) ) = t ∑ i = 1 (cid:11) i 1 ! i ( (cid:18) ) where the values (cid:11) 1 ; : : :; (cid:11) r are supposed known and (cid:11) r + 1 ; : : :; (cid:11) t unknown and 1 ! i is the indicator function. The uncertain part ( (cid:11) 1 ; : : :; (cid:11) r ) of the initial state x 0 is said to be ( \" 1 ; : : :; \" r ) -admissible if the sensitivity of corresponding output signal ( y i ) i 0 relatively to uncertainties ( (cid:11) k ) 1 ¬ k ¬ r is less to the treshold \" k , i.e., (cid:13)(cid:13)(cid:13)(cid:13)(cid:13) @ y i @(cid:11) k (cid:13)(cid:13)(cid:13)(cid:13)(cid:13) ¬ \" k , 8 i 0, 8 k 2 f 1 ; : : :; r g . The main goal of this paper is to determine the set of all possible gain operators that makes the system insensitive to all uncertainties. The characterization of this set is investigated and an algorithmic determination of each gain operators is presented. Some examples are given.","PeriodicalId":48654,"journal":{"name":"Archives of Control Sciences","volume":"30 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Control Sciences","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.24425/acs.2020.132589","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 5
Abstract
In this paper, we consider an infinite dimensional linear systems. It is assumed that the initial state of system is not known throughout all the domain Ω (cid:26) R n , the initial state x 0 2 L 2 ( Ω ) is supposed known on one part of the domain Ω and uncertain on the rest. That means Ω = ! 1 [ ! 2 [ : : : [ ! t with ! i \ ! j = ∅ , 8 i ; j 2 f 1 ; : : :; t g , i , j where ! i , ∅ and x 0 ( (cid:18) ) = (cid:11) i for (cid:18) 2 ! i , 8 i , i.e., x 0 ( (cid:18) ) = t ∑ i = 1 (cid:11) i 1 ! i ( (cid:18) ) where the values (cid:11) 1 ; : : :; (cid:11) r are supposed known and (cid:11) r + 1 ; : : :; (cid:11) t unknown and 1 ! i is the indicator function. The uncertain part ( (cid:11) 1 ; : : :; (cid:11) r ) of the initial state x 0 is said to be ( " 1 ; : : :; " r ) -admissible if the sensitivity of corresponding output signal ( y i ) i 0 relatively to uncertainties ( (cid:11) k ) 1 ¬ k ¬ r is less to the treshold " k , i.e., (cid:13)(cid:13)(cid:13)(cid:13)(cid:13) @ y i @(cid:11) k (cid:13)(cid:13)(cid:13)(cid:13)(cid:13) ¬ " k , 8 i 0, 8 k 2 f 1 ; : : :; r g . The main goal of this paper is to determine the set of all possible gain operators that makes the system insensitive to all uncertainties. The characterization of this set is investigated and an algorithmic determination of each gain operators is presented. Some examples are given.
期刊介绍:
Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.