A Data-Driven Hybrid ARX and Markov Chain Modeling Approach to Process Identification With Time-Varying Time Delays

作者：Zhao Yujia^*; Fatehi Alireza; Huang Biao

来源：IEEE Transactions on Industrial Electronics, 2017, 64(5): 4226-4236.

DOI：10.1109/TIE.2016.2597764

• AutoRegressive eXogenous (ARX) model
• expectation-maximization (EM) algorithm
• Markov chain
• process identification
• time delays

In this paper, we consider an important practical industrial process identification problem where the time delay can change at every sampling instant. We model the time-varying discrete time-delay mechanism by a Markov chain model and estimate the Markov chain parameters along with the time-delay sequence simultaneously. Besides time-varying delay, processes with both time-invariant and time-variantmodel parameters are also considered. The former is solved by an expectation-maximization (EM) algorithm, while the latter is solved by a recursive version of the EM algorithm. The advantages of the proposed identification methods are demonstrated by numerical simulation examples and an evaluation on pilot-scale experiments.

• 出版日期2017-5