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

Kohtaro Kosaka, Takashi Yoshimi, Takeya Izumikawa, Takashi Umeda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationASCC 2022 - 2022 13th Asian Control Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1143-1147
Number of pages5
ISBN (Electronic)9788993215236
DOIs
Publication statusPublished - 2022
Event13th Asian Control Conference, ASCC 2022 - Jeju, Korea, Republic of
Duration: 2022 May 42022 May 7

Publication series

NameASCC 2022 - 2022 13th Asian Control Conference, Proceedings

Conference

Conference13th Asian Control Conference, ASCC 2022
Country/TerritoryKorea, Republic of
CityJeju
Period22/5/422/5/7

Keywords

  • hydraulic excavator
  • large time delay
  • learning control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization

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