Parallel Precomputation with Input Value Prediction for Model Predictive Control Systems

Satoshi KAWAKAMI  Takatsugu ONO  Toshiyuki OHTSUKA  Koji INOUE  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.12   pp.2864-2877
Publication Date: 2018/12/01
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
parallel precomputation,  input value prediction,  approximate computing,  model predictive control,  real-time system,  

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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.