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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

Satoshi KAWAKAMI

Kyushu University

Takatsugu ONO

Kyushu University

Toshiyuki OHTSUKA

Kyoto University

Koji INOUE

Kyushu University

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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},}

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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 -