Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm |
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Authors: | Lei Chen Aditya Tulsyan Biao Huang Fei Liu |
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Affiliation: | 1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, PR China;2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 2G6, Canada |
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Abstract: | 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. |
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Keywords: | System identification Nonlinear process Multiple models Expectation maximization algorithm Kalman smoother Particle smoother |
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