Offline and Online Parameter Learning for Switching Multirate Processes With Varying Delays and Integrated Measurements

It is difficult to measure properties of certain key/quality variables at a fast rate due to technical constraints or economical considerations. Such variables are measured infrequently through laboratory analysis with a considerable delay. Also, sample collection for laboratory analysis may be exte...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) Vol. 69; no. 7; pp. 7213 - 7222
Main Authors: Salehi, Yousef, Huang, Biao
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Current measurement
Delays
Economic analysis
Expectation–maximization (EM) algorithm
Hybrid systems
identification
Input variables
Intervals
Laboratories
Parameter estimation
Prediction algorithms
Process controls
Real time
Real-time systems
Recursive functions
Regression models
Sampling
Switches
Switching
switching multirate system
Time measurement
varying delays
ISSN:0278-0046, 1557-9948
Online Access:Get full text
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Summary:It is difficult to measure properties of certain key/quality variables at a fast rate due to technical constraints or economical considerations. Such variables are measured infrequently through laboratory analysis with a considerable delay. Also, sample collection for laboratory analysis may be extended over a significant time interval. In this article, the objective is to solve the parameter estimation problem along with real-time output prediction for switching multirate sampled processes with unknown varying delays and unknown varying sampling intervals. First, under the framework of the expectation-maximization (EM) algorithm, offline parameter estimation problem of dual-rate switching augmented regression models is handled. The delays, sampling intervals, and operating modes are considered as the hidden variables modeled by a nonparametric-distribution-based approach. In addition, as the fast-rate prediction of the slow-rate sampled variables is often used for process control applications, a recursive EM algorithm is used to predict the fast-rate outputs in real time. The efficacy of the proposed algorithms is shown through an experimental study on a laboratory hybrid tank system. The results show a satisfactory fast-rate prediction of the slow-rate sampled variables, and the significance of considering different sampling intervals is highlighted. Also, estimates of the occurrence probabilities of each possible delay, sampling interval, and mode are obtained without being limited by the assumption of a prior distribution.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3095807