Shrinkage Prediction of a Steel Production via Model-On-Demand

Kishi, Masatomo; Kimura, Kazuyoshi; Ohta, Jun; Yamamoto, Shigeru

doi:10.1016/s1474-6670(17)31065-0

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…JIT modeling is also known as model-on-demand [8,9], lazy learning [10], or instance-based learning [11]. JIT modeling can be applied to a wide range of industrial applications, including steel industry [12][13][14][15], PID parameter tuning [16,17], soft sensors in industrial chemical processes [18].…”

Section: Introductionmentioning

confidence: 99%

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

Jiravit,

Yamamoto

2024

IEEJ Transactions Elec Engng

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One of the primary advantages of model‐free predictive control over conventional methods is that it does not use any mathematical models and relies only utilizes measured input/output data from the storage. The capability of model‐free predictive control has been already demonstrated in nonlinear systems using linear and polynomial regression for data storage. However, identifying the appropriate order that aligns with the actual system order remains a primary challenge, selecting an incorrect order may result in increasing redundant terms, ultimately leading to instability issues. In this study, we employed the Singular Value Decomposition (SVD) order selection technique, combined with the Bayesian Information Criterion (BIC), to identify the appropriate input and output orders of the system as well as the optimal horizon order in predictive control. This combined technique was subsequently applied to determine the appropriate order for model‐free predictive control. Our findings confirmed the effectiveness of the proposed method using numerical simulations in both linear and nonlinear systems. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Introductionmentioning

confidence: 99%

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

Jiravit,

Yamamoto

2024

IEEJ Transactions Elec Engng

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“…Just‐in‐time modeling is also referred to as model on‐demand , lazy learning , or instance‐based learning . Applications of just‐in‐time modeling include the prediction of production processes in the steel industry , proportional‐integral–derivative (PID) parameter tuning , and the use of soft sensors in industrial chemical processes . Application to predictive control has also been proposed .…”

Section: Introductionmentioning

confidence: 99%

Multi‐input multi‐output model‐free predictive control and its application to wastewater treatment

Yamamoto

2017

IEEJ Transactions Elec Engng

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Model‐free predictive control is a data‐driven control method that directly computes the control input from massive input/output datasets. It does not require the mathematical models that are used in conventional model predictive control. It has recently been shown that the control offered by model‐free predictive control can be improved by the introduction of polynomial regression vectors containing the control input and measurement output. In this paper, we extend these findings to multi‐input multi‐output nonlinear systems and investigate the effectiveness of the approach through numerical simulations of a wastewater treatment process. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Mechanical property control system for cold rolled steel sheet through locally weighted regression model

Shigemori¹

2013

2013 9th Asian Control Conference (ASCC)

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Shrinkage Prediction of a Steel Production via Model-On-Demand

Cited by 10 publications

References 5 publications

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

Multi‐input multi‐output model‐free predictive control and its application to wastewater treatment

Mechanical property control system for cold rolled steel sheet through locally weighted regression model

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