Tracked Vehicle Velocity Estimation by Disturbance Observer and Machine Learning, and its Application to Driving Force Control for Slippage Suppression

Hiroaki Kuwahara, Toshiyuki Murakami

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Tracked vehicles generally involve slippage owing to the interaction between the road and track surfaces, which renders accurate motion control difficult. This paper proposes a velocity estimation method for a tracked vehicle with slippage, and its application to driving force control. In this method, the disturbance estimated by a disturbance observer was used as information related to slippage, and a neural network was constructed for velocity estimation. In addition, a driving force observer was designed using the estimated velocity. The driving control of the tracked vehicle to suppress slippage was achieved by using the feedback of the estimated driving force. The proposed method was evaluated experimentally through the velocity estimation performance and slip suppression performance tests.

本文言語English
ページ(範囲)69-75
ページ数7
ジャーナルIEEJ Journal of Industry Applications
11
1
DOI
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • 自動車工学
  • エネルギー工学および電力技術
  • 機械工学
  • 産業および生産工学
  • 電子工学および電気工学

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