Scholarlink

<p>This paper introduces a novel control approach for automated excavators combining model-based and learning-based techniques to enhance control accuracy. The feedback linearization technique is employed based on error dynamics in designing boom and bucket velocity controllers incorporating the driver’s manual arm control. Additionally, supervised learning is used to approximate inverse hydraulic actuation system and to compute joystick control inputs corresponding to the desired control velocity. To further refine control precision reinforcement learning is used to optimize the driver’s manual arm manipulation within a given cycle time. The performance of the proposed methodology is demonstrated through simulations on a 30-ton excavator and compared with results based on model-based techniques.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhanced Hydraulic Excavator Control via Semi-automatic Grading Control Using Reinforcement Learning

  • 期刊论文

  • 发表日期：

    2025年3月10日

查看全文
  • Youngbum Kim,
  • Jinwhan Kim

摘要

This paper introduces a novel control approach for automated excavators combining model-based and learning-based techniques to enhance control accuracy. The feedback linearization technique is employed based on error dynamics in designing boom and bucket velocity controllers incorporating the driver’s manual arm control. Additionally, supervised learning is used to approximate inverse hydraulic actuation system and to compute joystick control inputs corresponding to the desired control velocity. To further refine control precision reinforcement learning is used to optimize the driver’s manual arm manipulation within a given cycle time. The performance of the proposed methodology is demonstrated through simulations on a 30-ton excavator and compared with results based on model-based techniques.