TY  - CHAP
KW  - Convex relaxation; LPV models; LPV realization theory; Set-membership identification 
N2  - Set-membership identification algorithms have been recently proposed to derive linear parameter-varying (LPV) models in input-output form, under the assumption that both measurements of the output and the scheduling signals are affected by bounded noise. In order to use the identified models for controller synthesis, linear time-invariant (LTI) realization theory is usually applied to derive a statespace model whose matrices depend statically on the scheduling signals, as required by most of the LPV control synthesis techniques. Unfortunately, application of the LTI realization theory leads to an approximate state-space description of the original LPV input-output model. In order to limit the effect of the realization error, a new set-membership algorithm for identification of input/output LPV models is proposed in the paper. A suitable nonconvex optimization problem is formulated to select the model in the feasible set which minimizes a suitable measure of the state-space realization error. The solution of the identification problem is then derived by means of convex relaxation techniques.
N1  - 2012 American Control Conference; held in Montréal Canada),
June 27-June 29, 2012   -    Best presentation in session award
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6315441&isnumber=6314593
ID  - eprints2446
TI  - Minimal LPV state-space realization driven set-membership identification
AV  - public
T2  - Proceedings of American Control Conference (ACC), 2012
A1  - Cerone, Vito
A1  - Piga, Dario
A1  - Regruto, Diego
A1  - Tóth, Roland
Y1  - 2012/06//
SP  - 3421
SN  - 978-1-4577-1095-7
PB  - IEEE
EP  - 3426
ER  -