Identification of errors-in-variables ARX model with time varying time delay

• Published in: Journal of process control Vol. 115; pp. 134 - 144
• Main Authors: Zhang, Jinxi, Guo, Fan, Hao, Kuangrong, Chen, Lei, Huang, Biao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2022
• Subjects: Errors-in-variables (EIV); Expectation maximization algorithm (EM); Parameter estimation; Time delays; Parameter estimation; Expectation maximization algorithm (EM); Time delays; Errors-in-variables (EIV)
• ISSN: 0959-1524
• DOI: 10.1016/j.jprocont.2022.04.019

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. •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.