在机器人动力学和激发策略中确定最小参数集的研究
Zhiqiang Wang1, Jianhai Han1,2, Xiangpan Li1,2
1School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究介绍了一种使用最小参数集进行机器人动态识别的方法. 该方法确保了准确的参数识别,并使机器人控制的精确前向动态模拟成为可能.
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:
- 机器人动态识别对于准确的建模和控制至关重要.
- 现有的方法可能缺乏效率或可识别性.
- 一个最小的参数集简化了动态模型.
研究的目的:
- 开发一种有效且可识别的机器人动态识别方法.
- 用螺丝理论和矩阵运算推导最小动态参数集.
- 通过前进的动态模拟和实验测试来验证该方法.
主要方法:
- 使用牛顿-欧勒法制定机器人动态.
- 螺丝理论用于动力学线性矩阵表示的应用.
- 使用克罗内克的产物和全等级分解来获得最小参数集.
- 序列关节激发和最小平方法用于参数识别.
- 使用已识别的参数进行前进动态模拟.
主要成果:
- 拟议的方法成功识别了最小的动态参数集.
- 顺序激发可确保准确的参数识别和机器人安全.
- 前进动态模拟准确地复制机器人的动力行为.
- 在平面3R机器人上的实验验证证证了该方法的有效性.
结论:
- 由此衍生出的最小动态参数集使得机器人能够高效且可识别的建模.
- 顺序激发策略对于逐步参数识别是有效的.
- 经过验证的方法提高了机器人控制和模拟的精度.
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