Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm

被引:62
作者
Chen, Lei [1 ,2 ]
Tulsyan, Aditya [2 ]
Huang, Biao [2 ]
Liu, Fei [1 ]
机构
[1] Jiangnan Univ, Inst Automat, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2G6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
System identification; Nonlinear process; Multiple models; Expectation maximization algorithm; Kalman smoother; Particle smoother; KALMAN SMOOTHER; PREDICTION; TUTORIAL;
D O I
10.1016/j.jprocont.2013.09.013
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed; wherein, a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identification of local ARX models, and for computing the probability associated with each of the local ARX models taking effect. A smoothing algorithm is used to estimate the distribution of the hidden scheduling variables in the EM algorithm. If the dynamics of the scheduling variables are linear, Kalman smoother is used; whereas, if the dynamics are nonlinear, sequential Monte-Carlo (SMC) method is used. Several simulation examples, including a continuous stirred tank reactor (CSTR) and a distillation column, are considered to illustrate the efficacy of the proposed method. Furthermore, to highlight the practical utility of the developed identification method, an experimental study on a pilot-scale hybrid tank system is also provided. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1480 / 1496
页数:17
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