Robust online identification for hybrid multirate systems based on recursive EM algorithm

• Published in: Journal of process control Vol. 153; p. 103514
• Main Authors: Guo, Fan, Huang, Biao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2025
• Subjects: Hybrid Multirate systems; Laplace distribution; Recursive expectation maximization algorithm; Robust online identification; Laplace distribution; Hybrid Multirate systems; Recursive expectation maximization algorithm; Robust online identification
• ISSN: 0959-1524
• DOI: 10.1016/j.jprocont.2025.103514

This paper focuses on robust identification for both linear time-invariant and time-variant multirate systems with time delays subject to outliers. The time delays are time varying and modeled by a Markov chain. Furthermore, the collected output data, which is corrupted by outliers, is described through a Laplace distribution. Parameters for the time-invariant model are estimated utilizing the batch expectation maximization (BEM) algorithm, whereas the recursive EM (REM) algorithm is employed for parameter estimation of the time-variant model. Upon receiving new data, the BEM first incorporates it in the historical batch data set and then iteratively recalculates parameter estimation using the updated data set. In contrast, the REM algorithm uses the parameter values obtained from the preceding step to recursively refine its estimates according to the new data sample. The efficacy of the proposed methods is demonstrated through a numerical example and a simulated continuous fermentation reactor process. •Laplace distribution is employed to model noises with outliers. Varying time delays are described by Markov chain.•Both BEM algorithm and REM algorithm are used to estimate the latent variables and unknown parameters, respectively.•The robust online method can achieve online parameter estimation, time-varying delay estimation and outlier detection.