TY  - CHAP
T2  - Proceedings of the 52nd Annual Conference on Decision and Control (CDC), 2013
EP  - 4115
ID  - eprints2459
N2  - 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.
SP  - 4110
A1  - Formentin, Simone
A1  - Piga, Dario
A1  - Tóth, Roland
A1  - Savaresi, Sergio M.
PB  - IEEE
SN  - 978-1-4673-5714-2 
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6760519&isnumber=6759837
TI  - Direct data-driven control of linear parameter-varying systems
AV  - public
KW  - Data models; Dynamic scheduling; Instruments; Noise measurement; Optimization; Trajectory; Tuning

Y1  - 2013/12//
ER  -