Learning Control Parameter for Transmissions Using Hierarchical Stochastic Optimization
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- KARASAWA Hiroyuki
- The University of Tokyo
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- KANEMAKI Tomohiro
- Komatsu
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- OOMAE Kei
- Komatsu
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- FUKUI Rui
- The University of Tokyo
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- NAKAO Masayuki
- The University of Tokyo
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- OSA Takayuki
- The University of Tokyo
Bibliographic Information
- Other Title
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- 階層型確率的最適化を用いたトランスミッションの制御パラメータの学習
- -Simulation-based Parameter Selection-
- ―シミュレータによる条件候補の選定―
Abstract
<p>In many mechanical systems, control parameters are tuned by a human expert through trial and error, which is labor-intensive and time-consuming. For example, electronically controlled transmissions (ECT) require such parameter optimization. To address this issue, we propose a parameter optimization system for ECT by using Hierarchical Stochastic Optimization (HSO) that is able to handle multimodal objective function. The optimizer learns better parameters which show high peformance in all metrics and satisfy all constraints. In the experiments, we use multi-physics simulators and optimize the parameters for ECT. Through experiments, we demonstrate that our HSO can identify several modes of the objective function and is more sample-efficient than random search and a human operator.</p>
Journal
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- The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)
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The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (0), 1A1-L07-, 2019
The Japan Society of Mechanical Engineers
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Keywords
Details 詳細情報について
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- CRID
- 1390283659833097088
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- NII Article ID
- 130007774224
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- ISSN
- 24243124
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed