基于智能学习的滑动参数估计的自适应性强型控制,用于轮式移动机器人
M H Korayem1, M Safarbali1, N Yousefi Lademakhi1
1Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran.
ISA transactions
|February 23, 2024
概括
这项研究引入了一种智能方法,用于估计在轮式移动机器人 (WMR) 中的车轮滑动. 这种方法显著减少了跟踪错误,将机器人的稳定性和路径精度提高了26%.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 轮式移动机器人 (WMR) 面临着轮子滑动的挑战,影响稳定性和路径遵循.
- 准确地确定和控制轮滑是可靠机器人操作的关键.
- 现有的控制方法在动态环境中与不确定性和外部干扰作斗争.
研究的目的:
- 提出一种智能方法来估计WMR中的纵向和横向轮滑.
- 开发一种适应性强大的控制器,以弥补轮子的滑动,不确定性和干扰.
- 证明拟议的滑行估计和控制战略的有效性和可行性.
主要方法:
- 利用在不同数据集上训练的三个回归网络 (人工神经网络 - ANN) 来估计滑动比率和侧滑角度.
- 开发了一个动态的WMR模型,包括轮滑和修改的引力.
- 实现了基于滑动模式控制 (SMC) 的自适应性强型控制器,以提高性能.
主要成果:
- 智能滑动估计器在预测车轮在各种地形和机动中滑动时实现了高准确度.
- 拟议的自适应性强控制器在减轻外部干扰和不确定性方面表现优于标准SMC.
- 综合系统将跟踪错误平均减少26%,而不是循环轨迹的滑动补偿方法.
结论:
- 拟议的智能滑动估计和自适应强大的控制策略有效地提高了WMR的性能.
- 这种方法显著提高了路径跟踪的准确性和稳定性,特别是在具有挑战性的条件下.
- 该方法为需要精确导航的现实世界WMR应用提供了强大的解决方案.
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