Refined instrumental variable parameter estimation of continuous-time Box-Jenkins models from irregularly sampled data

Fengwei Chen, Hugues Garnier, Marion Gilson, Juan C. Agüero, Tao Liu

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

© 2016 The Institution of Engineering and Technology. This study investigates the estimation of continuous-time Box-Jenkins model parameters from irregularly sampled data. The Box-Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two submodels that feature the characteristics of both plant and noise systems. Based on plant-noise model decomposition, a two-step iterative procedure is proposed to solve the estimation problem, which consists of an instrumental variable method for the plant model and a prediction error method for the noise model. The proposed method is of low complexity and shows good estimation robustness and accuracy. Implementation issues are discussed to improve the computational efficiency. Numerical examples are presented to demonstrate the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)291-300
Number of pages10
JournalIET Control Theory and Applications
DOIs
Publication statusPublished - 20 Jan 2017

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