Asymptotic statistical analysis for model-based control design strategies

作     者：Esparza, Alicia Agueero, Juan C. Rojas, Cristian R. Godoy, Boris I. 

作者机构：Univ Newcastle Ctr Complex Dynam Syst & Control CDSC Newcastle NSW 2308 Australia Univ Politecn Valencia Dept Syst Engn & Control Valencia 46022 Spain Royal Inst Technol KTH ACCESS Linnaeus Ctr SE-10044 Stockholm Sweden 

出 版 物：《AUTOMATICA》 (自动学)

年 卷 期：2011年第47卷第5期

页      面：1041-1046页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

基　　金：Generalitat Valenciana (Spain) [GVPRE/2008/116] 

主　　题：System identification Maximum likelihood Model-based control 

摘      要：In this paper, we generalize existing fundamental limitations on the accuracy of the estimation of dynamic models. In addition, we study the large sample statistical behavior of different estimation-based controller design strategies. In particular, fundamental limitations on the closed-loop performance using a controller obtained by Virtual Reference Feedback Tuning (VRFT) are studied. We also extend our results to more general estimation-based control design strategies. We present numerical examples to show the application of our results. (C) 2011 Elsevier Ltd. All rights reserved.