TY  - JOUR
KW  - Linear parameter-varying systems; Parameter estimation; Instrumental variables; System identification
JF  - Automatica
UR  - http://www.sciencedirect.com/science/article/pii/S0005109815000199
A1  - Piga, Dario
A1  - Cox, Pepijn
A1  - Tóth, Roland
A1  - Laurain, Vincent
Y1  - 2015/03//
VL  - 53
PB  - Elsevier 
ID  - eprints2571
EP  - 338
TI  - LPV system identification under noise corrupted scheduling and
output signal observations
SP  - 329
N2  - Most of the approaches available in the literature for the identification of Linear Parameter-Varying (LPV) systems rely on the assumption that only the measurements of the output signal are corrupted by the noise, while the observations of the scheduling variable are considered to be noise free. However, in practice, this turns out to be an unrealistic assumption in most of the cases, as the scheduling variable is often related to a measured signal and, thus, it is inherently affected by a measurement noise. In this paper, it is shown that neglecting the noise on the scheduling signal, which corresponds to an error-invariables
problem, can lead to a significant bias on the estimated parameters. Consequently, in order to overcome this corruptive phenomenon affecting practical use of data-driven LPV modeling, we present an identification scheme to compute a consistent estimate of LPV Input/Output (IO) models from noisy output and scheduling signal observations. A simulation example is provided to prove the effectiveness
of the proposed methodology.
AV  - none
SN  - 0005-1098
ER  -