Parallel Precomputation with Input Value Prediction for Model Predictive Control Systems

• KAWAKAMI Satoshi

  Graduate School of Information Science and Electrical Engineering, Kyushu University

• ONO Takatsugu

  Graduate School of Information Science and Electrical Engineering, Kyushu University

• OHTSUKA Toshiyuki

  Graduate School of Informatics, Kyoto University

• INOUE Koji

  Graduate School of Information Science and Electrical Engineering, Kyushu University

抄録

We propose a parallel precomputation method for real-time model predictive control. The key idea is to use predicted input values produced by model predictive control to solve an optimal control problem in advance. It is well known that control systems are not suitable for multi- or many-core processors because feedback-loop control systems are inherently based on sequential operations. However, since the proposed method does not rely on conventional thread-/data-level parallelism, it can be easily applied to such control systems without changing the algorithm in applications. A practical evaluation using three real-world model predictive control system simulation programs demonstrates drastic performance improvement without degrading control quality offered by the proposed method.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E101.D (12), 2864-2877, 2018-12-01

  一般社団法人 電子情報通信学会

キーワード

• parallel precomputation
• input value prediction
• approximate computing
• model predictive control
• real-time system

詳細情報

• CRID: 1390845713025433728
• NII論文ID: 130007539327
• NII書誌ID: AA11510321
• DOI: 10.1587/transinf.2018pap0003
• ISSN: 17451361
• ISSN: 09168532
• HANDLE: 2324/7174313
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E101.D/12/E101.D_2018PAP0003/_pdf
• 本文言語コード: en
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