JSAI2024

Presentation information

Organized Session

Organized Session » OS-15

[1K4-OS-15a] OS-15

Tue. May 28, 2024 3:00 PM - 4:40 PM Room K (Room 44)

オーガナイザ:鷲尾 隆(大阪大学)、西山 直樹、吉岡 琢(株式会社Laboro.AI)、小松崎 民樹(北海道大学)、山崎 啓介(産業技術総合研究所)、窪澤 駿平(日本電気株式会社)

4:20 PM - 4:40 PM

[1K4-OS-15a-05] Survey of machine learning-based modeling for process control systems

〇Junya Ikemoto1,3, Shumpei Kubosawa1,3, Takashi Onishi1,3, Yoshimasa Tsuruoka2,3 (1. NEC Corporation, 2. Univ. of Tokyo, 3. NEC-AIST AI Cooperative Research Laboratory)

Keywords:Modeling, Inductive Bias, Process Control

This paper presents a survey on machine learning modeling for process control systems. Model-based control methods such as model predictive control are useful for automation of industrial plants because they can optimize their control policies efficiently with mathematical models of plants. In such methods, it is important to obtain models that can predict behaviors of plants accurately. Recently, machine learning-based modeling methods have attracted much attention to identify nonlinear plants. In this paper, we summarize standard machine learning-based approaches and introduce usage of prior knowledge such as scientific or common knowledge. Additionally, as an example in the real world, we introduce model predictive control with neural network modeling to deal with disturbance for a distillation plant. Finally, future work of machine learning-based modeling for industrial plants are analyzed and discussed.

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