Two nonlinear optimization methods for black box identification compared

作     者：Van Mulders, Anne Schoukens, Johan Volckaert, Marnix Diehl, Moritz 

作者机构：Vrije Univ Brussel Dept ELEC B-1050 Brussels Belgium Katholieke Univ Leuven Dept MECH B-3001 Louvain Belgium Katholieke Univ Leuven Dept ESAT B-3001 Louvain Belgium 

出 版 物：《AUTOMATICA》 (自动学)

年 卷 期：2010年第46卷第10期

页      面：1675-1681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：Flemish Government [METH-1] Belgian State, Prime Minister's Office, Science Policy [IUAP VI/4 - Dysco] Research council of the Vrije Universiteit Brussel (OZR) Fund for Scientific Research (FWO - Vlaanderen) [G.0320.08, G.0558.08] IOE-SCORES4CHEM ICCoS EU Helmholtz viCERP Comet ACCM KUL EF/05/006 (OPTEC) 

主　　题：Identification algorithms Parameter estimation Nonlinear systems Nonlinear models State-space models Constraints 

摘      要：In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate the parameters of e.g. a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization of the same cost function. While the first method does not estimate the states explicitly, the second method estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation data. The unconstrained method appears to be faster and more memory efficient, but the constrained method has a significant advantage as well: it is robust for unstable systems of which bounded input-output data can be measured (e.g. a system captured in a stabilizing feedback loop). Both methods have successfully been applied on real-life measurement data. (C) 2010 Elsevier Ltd. All rights reserved.