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论文类型：期刊论文

发表时间：2017-02-01

发表刊物：JOURNAL OF PROCESS CONTROL

收录刊物：SCIE、EI、Scopus

卷号：50

页面范围：40-55

ISSN号：0959-1524

关键字：Output error model; Time delay; Load disturbance; Convergence; Forgetting factor

摘要：In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open- and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method. (C) 2016 Elsevier Ltd. All rights reserved.