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A study on automatic control of wheel loaders in rock/soil loading

机译:轮式装载机在岩土中自动控制的研究

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摘要

Wheel loaders are widely used in mines, construction projects, and waste processing fields due to their merits of high mobility, remarkable operational flexibility, and relative low capital cost. Automatic control of the loading process will achieve high productivity, solve the problem of shortage of experienced operators, release human workers from hazardous working environments, and reduce maintenance cost associated with machine abuse. Existing research on wheel loader control only automate the manipulator mechanism while leave the locomotion system to human operators. The research in this dissertation focuses on the development of a digging controller that can simultaneously control bucket motion and vehicle advancement, and can adapt its parameters in different digging conditions. In this controller, a digging task is decomposed into three sequentially implemented operations: attacking, crowding, and scooping; each of them can be carried out with different strategies. An ideal trajectory is pre-generated based on the physical property of the wheel loader and rock pile, and is generally followed during the digging process. Human operators' practice in regulating vehicle speed and coordinating bucket motion and vehicle advancement is intensively investigated, refined into control rules, and implemented with intelligent and conventional control methods. Machine features are analyzed in depth and incorporated into control algorithm design. The investigation on the non-trivial time-delay characteristic of the manipulator hydraulic system leads to the building of dynamic models and the application of predictive control principle in digging decision-making. The feature of small working space of wheel loader was considered in bucket trajectory generation and instant bucket motion planning. The research on wheel slip occurrence leads to a detection algorithm being developed and a slip restrain strategy being presented. Self-adaptation mechanism is constructed in which trajectory tracking-quality is applied for evaluating digging control performance, statistical analysis is applied for improving the accuracy on bucket penetration depth prediction, and machine-learning technique is applied for revising the membership function of fuzzy consequent variables. Simulation experiments show that the controller works effectively in rock piles with different conditions, from fine sand and soil to big fragmented rock pieces.
机译:轮式装载机具有机动性高,操作灵活性强和投资成本相对较低等优点,因此广泛用于矿山,建筑项目和废物处理领域。装载过程的自动控制将实现高生产率,解决经验丰富的操作员短缺的问题,将工人从危险的工作环境中解放出来,并减少与机器滥用相关的维护成本。轮式装载机控制的现有研究仅使机械手机构自动化,而将运动系统留给操作员。本文的研究重点是挖掘控制器的开发,该控制器可以同时控制铲斗的运动和车辆的前进,并可以在不同的挖掘条件下适应其参数。在此控制器中,挖掘任务被分解为三个顺序执行的操作:攻击,拥挤和挖取;他们每个人都可以使用不同的策略来执行。理想的轨迹是根据轮式装载机和岩石堆的物理特性预先生成的,通常在挖掘过程中会遵循。对驾驶员在调节车速,协调铲斗运动和车辆前进方面的实践进行了深入研究,将其完善为控制规则,并采用智能和常规控制方法来实施。机器特征进行了深入分析,并纳入了控制算法设计。对机械手液压系统非平凡的时延特性的研究导致了动力学模型的建立以及预测控制原理在挖掘决策中的应用。在铲斗轨迹生成和即时铲斗运动计划中考虑了轮式装载机工作空间小的特点。对车轮打滑发生的研究导致开发了一种检测算法并提出了一种防滑策略。构建自适应机制,其中轨迹跟踪质量用于评估挖掘控制性能,统计分析用于提高铲斗穿透深度预测的精度,机器学习技术用于修正模糊结果变量的隶属函数。仿真实验表明,该控制器在不同条件下的岩石桩(从细沙和土壤到大块碎石)中均能有效工作。

著录项

  • 作者

    Wu Long;

  • 作者单位
  • 年度 2003
  • 总页数
  • 原文格式 PDF
  • 正文语种 en_US
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