IEICE TRANSACTIONS on Information
Parallel Precomputation with Input Value Prediction for Model Predictive Control Systems
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.12 pp.2864-2877
- Publication Date
- 2018/12/01
- Publicized
- 2018/09/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018PAP0003
- Type of Manuscript
- Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
- Category
- Real-time Systems
Authors
Satoshi KAWAKAMI
Kyushu University
Takatsugu ONO
Kyushu University
Toshiyuki OHTSUKA
Kyoto University
Koji INOUE
Kyushu University
Keyword
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Satoshi KAWAKAMI, Takatsugu ONO, Toshiyuki OHTSUKA, Koji INOUE, "Parallel Precomputation with Input Value Prediction for Model Predictive Control Systems" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 2864-2877, December 2018, doi: 10.1587/transinf.2018PAP0003.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018PAP0003/_p
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@ARTICLE{e101-d_12_2864,
author={Satoshi KAWAKAMI, Takatsugu ONO, Toshiyuki OHTSUKA, Koji INOUE, },
journal={IEICE TRANSACTIONS on Information},
title={Parallel Precomputation with Input Value Prediction for Model Predictive Control Systems},
year={2018},
volume={E101-D},
number={12},
pages={2864-2877},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018PAP0003},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Parallel Precomputation with Input Value Prediction for Model Predictive Control Systems
T2 - IEICE TRANSACTIONS on Information
SP - 2864
EP - 2877
AU - Satoshi KAWAKAMI
AU - Takatsugu ONO
AU - Toshiyuki OHTSUKA
AU - Koji INOUE
PY - 2018
DO - 10.1587/transinf.2018PAP0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - 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.
ER -
English
