Authors: Jian-hong Wang; Yan-xiang Wang
Addresses: School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen, 333403, China ' School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen, 333403, China
Abstract: In this paper, we study the problem of the model structure validation for closed-loop system identification. Two probabilistic model uncertainties and optimum input filter are derived from some statistical properties of the parameter estimation. The probabilistic bounds and optimum input filter are based on an asymptotic normal distribution of the parameter estimator and its covariance matrix, which was estimated from sampled data. The uncertainties bounds of the model parameter and cross-correlation function are constructed in the probability sense by using the inner product form of the asymptotic covariance matrix. Further, the input filter is derived from the point of optimisation. Finally, the simulation example results confirm the identification theoretical results.
Keywords: closed-loop identification; model uncertainty; model structure validity; input filter.
International Journal of Modelling, Identification and Control, 2017 Vol.27 No.4, pp.323 - 331
Received: 04 Dec 2015
Accepted: 27 Jun 2016
Published online: 09 Jun 2017 *