Machine Learning Based Virtual Design Process for Optimal Control of Combustion Engine

機械学習を用いたバーチャル最適制御設計プロセス

This paper considers machine learning based virtual design process of engine control system, and the demonstration in a diesel engine air path control is shown. This process contains two steps of machine learning. In the first step, a control-oriented forward model that predicts the transient behavior of the engine is learned from detailed engine model by using recurrent neural network (RNN). In the second step, an inverse model that determines the optimal control inputs to follow the references is learned from the numerical computation results of the offline model predictive control (MPC). The forward and inverse models could be used as a state observer and a controller, respectively, in a control system. An experiment of a diesel air path control system designed by the process was conducted using rapid control prototyping (RCP), and its following capability to the reference was demonstrated.実機レスでの最適制御設計プロセスを目指し，機械学習を用いたディーゼルエンジン吸排気系制御を検討した．エンジンの詳細モデルから制御用順モデルを学習し，同モデルを用いたモデル予測制御をオフライン実行した結果から逆モデルを学習した．両モデルを用いた制御システムを構築し，目標値への追従能力を実験で検証した．

• Record URL:
  https://doi.org/10.11351/jsaeronbun.49.1162
• Record URL:
  http://scholar.google.com/scholar_lookup?title=Machine%20Learning%20Based%20Virtual%20Design%20Process%20for%20Optimal%20Control%20of%20Combustion%20Engine&author=R.%20Moriyasu&author=M.%20Ueda&author=M.%20Nagaoka&author=T.%20Ikeda&author=K.%20Nishikawa&author=S.%20Nojiri&author=T.%20Jimbo&author=A.%20Matsunaga&author=T.%20Nakamura&publication_year=2018
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
• Authors:
  • Moriyasu, Ryuta
  • Ueda, Matsuei
  • Nagaoka, Makoto
  • Ikeda, Taro
  • Nishikawa, Kazuaki
  • Nojiri, Sayaka
  • Jimbo, Tomohiko
  • Matsunaga, Akio
  • Nakamura, Toshihiro
• Publication Date: 2018-11

Language

• English
• Japanese

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1162-1166
• Serial:
  • Transactions of Society of Automotive Engineers of Japan
  • Volume: 49
  • Issue Number: 6
  • Publisher: Society of Automotive Engineers of Japan
  • ISSN: 0287-8321
  • EISSN: 1883-0811
  • Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Air intakes; Control systems; Diesel engines; Machine learning; Neural networks; Vehicle design
• Uncontrolled Terms: Model predictive control
• Subject Areas: Design; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01689409
• Record Type: Publication
• Source Agency: Japan Science and Technology Agency (JST)
• Files: TRIS, JSTAGE
• Created Date: Dec 20 2018 3:33PM