Direct data-driven control of linear parameter-varying systems

S. Formentin, D. Piga, R. Toth, S.M. Savaresi

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

8 Citaten (Scopus)


In many control applications, nonlinear plants can be modeled as linear parameter-varying (LPV) systems, by which the dynamic behavior is assumed to be linear, but also dependent on some measurable signals, e.g., operating conditions. When a measured data set is available, LPV model identification can provide low complexity linear models that can embed the underlying nonlinear dynamic behavior of the plant. For such models, powerful control synthesis tools are available, but the way the modeling error and the conservativeness of the embedding affect the control performance is still largely unknown. Therefore, it appears to be attractive to directly synthesize the controller from data without modeling the plant. In this paper, a novel data-driven synthesis scheme is proposed to lay the basic foundations of future research on this challenging problem. The effectiveness of the proposed approach is illustrated by a numerical example.
Originele taal-2Engels
TitelProceedings of the 52nd Conference on Decision and Control, 10-13 December 2013, Florence, Italy
StatusGepubliceerd - 2013
Evenement52nd IEEE Conference on Decision and Control (CDC 2013) - Florence, Italië
Duur: 10 dec 201313 dec 2013
Congresnummer: 52


Congres52nd IEEE Conference on Decision and Control (CDC 2013)
Verkorte titelCDC 2013
Ander52nd IEEE Conference on Decision and Control


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