相关实验视频
Updated: Sep 12, 2025

Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
一个连续操纵器的基于模型的控制与在线雅可比安错误补偿使用卡尔曼过的卡尔曼过
Yujia Zhai1, Jihao Xu1, Hangjie Mo2
1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China.
这项研究引入了肌驱动连续机器人的混合控制方法,提高了追踪精度和稳定性,而没有先前的数据. 这种方法可以提高适应性机器人系统的实时性能.
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
- 机械工程 机械工程
背景情况:
- 灵活的连续机器人具有很高的适应性,但由于非线性,在准确的建模和控制方面面临挑战.
- 现有的方法通常需要广泛的数据收集和培训,这限制了它们的实时适用性.
研究的目的:
- 为肌驱动连续机器人提出基于混合模型和在线数据驱动的控制方法.
- 为了提高连续机器人控制的准确性和效率,而不需要先前的数据集要求.
主要方法:
- 这是一种混合方法,它结合了断面恒定曲率模型与使用卡尔曼波器在线雅可比式错误补偿.
- 在连续的雅可比估计上实施约束,以确保实时稳定性和减少波动.
主要成果:
- 拟议的混合方法显著提高了连续机器人的追踪精度.
- 该方法证明了对外部干扰的稳定性,验证了其实时有效性.
- 实施不需要先前收集数据集或线下培训.
结论:
- 基于混合模型和在线数据驱动的控制方法对肌驱动的连续机器人有效.
- 这种方法为提高可适应机器人系统的性能和稳定性提供了一种实际解决方案.
- 该方法推进了复杂的非线性机器人系统的实时控制策略.
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