Data‐driven generalized minimum variance regulatory control using routine operation data
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- Ryota Uematsu
- Department of Mechanical Systems Engineering, Graduate School of Systems Design Tokyo Metropolitan University Tokyo Japan
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- Shiro Masuda
- Department of Mechanical Systems Engineering, Graduate School of Systems Design Tokyo Metropolitan University Tokyo Japan
抄録
<jats:title>Abstract</jats:title><jats:p>This paper provides a new generalized minimum variance (GMV) control using routine operation data. The proposed method achieves disturbance rejection without additional experiments and reference models, which is different from other data‐driven techniques. In this paper, a new data‐driven criterion is proposed for the Box and Jenkins (BJ) model, which is a more general description including the Auto‐Regressive and Moving Average eXogeneous (ARMAX) model. The paper proves that the optimization of the proposed criterion can achieve GMV control. Numerical examples for two different model structures show the validity of the proposed method. In particular, the application to datasets obtained from a continuous stirred tank reactor (CSTR) demonstrates the efficiency of the proposed method.</jats:p>
収録刊行物
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- Asian Journal of Control
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Asian Journal of Control 25 (1), 40-53, 2022-01-28
Wiley
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キーワード
詳細情報 詳細情報について
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- CRID
- 1360857593728974976
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- ISSN
- 19346093
- 15618625
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- データソース種別
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- Crossref
- KAKEN