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Indexed by:期刊论文

Date of Publication:2017-01-20

Journal:IET CONTROL THEORY AND APPLICATIONS

Included Journals:SCIE、EI

Volume:11

Issue:2

Page Number:291-300

ISSN No.:1751-8644

Key Words:sampled data systems; parameter estimation; continuous time systems; iterative methods; computational efficiency; noise model; prediction error method; plant model; instrumental variable method; two-step iterative procedure; plant-noise model decomposition; Box-Jenkins structure; irregularly sampled data; continuous-time Box-Jenkins model parameter estimation

Abstract: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 sub-models 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.