A Basic Study on Trajectory Accuracy Improvement of Hydraulic Excavator by Learning Control

Kohtaro Kosaka, Takashi Yoshimi, Takeya Izumikawa, Takashi Umeda

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Learning control is a method of compensating for reproducible errors due to repetitive movements by feedforwarding. The greatest advantage of this control method is that even if the dynamics of the controlled object is unknown, compensation can be performed from the input / output relationship. However, the conventional learning control is a method for robot arms with little time delay and it was difficult for providing sufficient compensation of the trajectory error of the hydraulic excavator which includes large time delay in its control system. Therefore, we considered and proposed the effective learning control method which could be applied to a system with a large delay. It is averaging the output error from each control time to the system delay time ahead. In this paper, we verified the basic effectiveness of proposed learning control by applying it to the experimental set-up of a hydraulic excavator.

本文言語English
ホスト出版物のタイトルASCC 2022 - 2022 13th Asian Control Conference, Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1143-1147
ページ数5
ISBN(電子版)9788993215236
DOI
出版ステータスPublished - 2022
イベント13th Asian Control Conference, ASCC 2022 - Jeju, Korea, Republic of
継続期間: 2022 5月 42022 5月 7

出版物シリーズ

名前ASCC 2022 - 2022 13th Asian Control Conference, Proceedings

Conference

Conference13th Asian Control Conference, ASCC 2022
国/地域Korea, Republic of
CityJeju
Period22/5/422/5/7

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 制御と最適化

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