Identification for control of multivariable systems: Controller validation and experiment design via LMIs
2008 (English)In: Automatica, ISSN 0005-1098, Vol. 44, no 12, 3070-3078 p.Article in journal (Refereed) Published
This paper presents a new controller validation method for linear multivariable time-invariant models. Classical prediction error system identification methods deliver uncertainty regions which are nonstandard in the robust control literature. Our controller validation criterion computes an upper bound for the worst case performance, measured in terms of the H-infinity-norm of a weighted closed loop transfer matrix, achieved by a given controller over all plants in such uncertainty sets. This upper bound on the worst case performance is computed via an LMI-based optimization problem and is deduced via the separation of graph framework. Our main technical contribution is to derive, within that framework, a very general parametrization for the set of multipliers corresponding to the nonstandard uncertainty regions resulting from PE identification of MIMO systems. The proposed approach also allows for iterative experiment design. The results of this paper are asymptotic in the data length and it is assumed that the model structure is flexible enough to capture the true system.
Place, publisher, year, edition, pages
2008. Vol. 44, no 12, 3070-3078 p.
Identification for robust control, Experiment design, Closed loop system identification, Controller validation, Multivariable plants, Linear multivariable feedback, LMI optimization
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-33080DOI: 10.1016/j.automatica.2008.05.022ISI: 000261964800009ScopusID: 2-s2.0-56349110432OAI: oai:DiVA.org:kth-33080DiVA: diva2:418999
QC 201507242011-05-252011-04-282015-07-24Bibliographically approved