We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Refined instrumental variable parameter estimation of continuous-time Box–Jenkins models from irregularly sampled data.
- Authors
Tao Liu; Fengwei Chen; Garnier, Hugues; Gilson, Marion; Agüero, Juan C.
- 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 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.
- Subjects
MATHEMATICAL variables; VECTOR error-correction models; QUANTITATIVE research; ANALYSIS of variance; POPULATION statistics
- Publication
IET Control Theory & Applications (Wiley-Blackwell), 2017, Vol 11, Issue 2, p291
- ISSN
1751-8644
- Publication type
Article
- DOI
10.1049/iet-cta.2016.0506