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Iterative method for frequency domain identification of continuous processes with delay time
Author(s) -
Jin Qibing,
He En,
Wang Qi,
Jiang Beiyan
Publication year - 2016
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22639
Subject(s) - transfer function , robustness (evolution) , frequency domain , control theory (sociology) , nonlinear system , time domain , taylor series , convergence (economics) , computer science , identification (biology) , term (time) , iterative method , system identification , least squares function approximation , algorithm , mathematics , mathematical optimization , control (management) , engineering , data modeling , statistics , database , artificial intelligence , estimator , economic growth , mathematical analysis , chemistry , biology , biochemistry , quantum mechanics , computer vision , physics , botany , electrical engineering , economics , gene
Delay time that affects the performances of many control synthesis techniques in controlled systems is common in most chemical industries. Estimating the delay time is also a difficult problem in identification fields. In this paper, aiming at continuous processes with delay time, an iterative least square identification method is proposed in the frequency domain. By introducing a truncated first‐order Taylor expansion, the delay time term is linearized. Linear regression equation for least squares (LS) is directly derived from the transfer function whose nonlinear term is replaced by a linear one. For reducing the linear approximation error and getting more accurate estimations of the model parameters, an iterative algorithm is developed based on the LS. Moreover, the proposed method can be easily extended to a closed‐loop system without increasing the order of the identified model. Simulations verify the effectiveness, fast convergence rates, and robustness of the proposed identification algorithm.