Premium
SM identification of approximating models for H ∞ robust control
Author(s) -
Giarré L.,
Milanese M.
Publication year - 1999
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/(sici)1099-1239(199905)9:6<319::aid-rnc407>3.0.co;2-v
Subject(s) - parametric statistics , identification (biology) , bounded function , system identification , set (abstract data type) , measure (data warehouse) , parametric model , mathematics , control theory (sociology) , computer science , robust control , domain (mathematical analysis) , mathematical optimization , algorithm , control (management) , control system , engineering , statistics , data mining , mathematical analysis , artificial intelligence , botany , electrical engineering , biology , programming language
Set Membership (SM) H ∞ identification of mixed parametric and non‐parametric models is investigated, aimed to estimate a low‐order approximating model and an identification error, giving a measure of the unmodelled dynamics in a form well suited for H ∞ control methodologies. In particular, the problem of estimating the parameters of the parametric part and the H ∞ bound on the modelling error is solved using frequency domain data, supposing l ∞ bounded measurement errors and that the system to be identified is exponentially stable. The effectiveness of the proposed procedure is tested on some numerical examples, showing the advantages of the proposed methods over the existing non‐parametric H ∞ identification approaches, in terms of lower model order and of tightness in the modelling error bounds. Copyright © 1999 John Wiley & Sons, Ltd.