Closed-loop subspace identification based on KPLS

Author

Lin Wen-yi ; Gu Yong ; Xie Lei

Author_Institution

State Key Lab. of Ind. Control Technol., Zhejiang Univ., Hangzhou, China

fYear

2013

Firstpage

1843

Lastpage

1848

Abstract

Closed-loop subspace identification methods have enjoyed tremendous development in last decade. This paper presents a novel method combined with KPLS, aiming at the situation without persistence of excitation. In this method, KPLS is utilized to obtain Markov parameters, then, state sequence is estimated by SVD decomposition. Based on the estimated state sequence, the model parameters are estimated by linear regression. 30 Monte Carlo simulation examples are presented in the end of the paper, the results are shown to be competitive and robust in the situation without persistence of excitation in MIMO system, and the new method is more applicable for process industry.

Keywords

MIMO systems; Markov processes; Monte Carlo methods; closed loop systems; identification; regression analysis; KPLS; MIMO system; Markov parameters; Monte Carlo simulation examples; SVD decomposition; closed-loop subspace identification; linear regression; process industry; state sequence; Electronic mail; Laboratories; Least squares methods; MIMO; Markov processes; Monte Carlo methods; Reactive power; Closed-loop subspace identification; KPLS; SVD decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2013 32nd Chinese

Conference_Location

Xi´an

Type

conf

Filename

6639727