Identification of errors-in-variables ARX model with time varying time delay. (July 2022)

• Record Type: Journal Article
• Authors: Zhang, Jinxi; Guo, Fan; Hao, Kuangrong; Chen, Lei; Huang, Biao
• Is Part Of: Journal of process control. Volume 115(2022)
• Page Start: 134
• Page End: 144
• Publication Date: 2022-07
• Subjects: Parameter estimation -- Expectation maximization algorithm (EM) -- Time delays -- Errors-in-variables (EIV); Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.04.019
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: An identification method is proposed for errors-in-variables (EIV) ARX model with input time-varying time-delays. A Markov chain is used to model varying time delays whose parameters are also estimated. The EIV system accounts for noises in both input and output. To estimate noise-free input, a linear state space model is used to describe input generation process and a Kalman smoother is adopted for its estimation. An expectation maximization algorithm is used to estimate ARX model parameters. A spinning process of polyester fiber and a continuous stirred tank reactor process are used to verify the effectiveness of the proposed approach. Highlights: Time-varying time-delays are considered for Errors-in-variables (EIV) system. Kalman smoother is used to estimation the noise-free input. The EM algorithm is used to estimate the EIV-TD system parameters and update the noise variance of the input data. Two simulations are provided for performance evaluation.