FOESO-Net:用于快速无传感器机器人操纵器扭矩估计的特定神经网络
Shike Long1, Xuanju Dang2, Jia Huang2
1School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; School of Aeronautics and Astronautics, Guilin University of Aerospace technology, Guilin 541004, China.
本研究介绍了FOESO-Net,这是一个用于机器人扭矩估计的新型分数顺序扩展状态观察员网络. 它通过改善实时碰撞检测和减少估计错误,提高了人机协作中的安全性和性能.
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
- 机械电子学是什么意思 机械电子学
背景情况:
- 接触扭矩传感对于安全的人机协作和碰撞检测至关重要.
- 现有的无传感器扭矩估计方法与噪声和有限的带宽作斗争,阻碍了快速,非线性扭矩观察.
- 传统的方法往往需要复杂的计算,如联合加速和逆惯性矩阵.
研究的目的:
- 为机器人操纵器开发一种先进的无传感器扭矩估计方法.
- 解决线性观察员在处理杂和快速变化时间的非线性扭矩方面的局限性.
- 提高协作机器人的安全性和响应能力.
主要方法:
- 提出了一个新的分数顺序扩展状态观察器 (FOESO),利用动量作为估计的基准状态.
- 设计了一个定制的网络,FOESO-Net,结合了分数顺序和动态基于重量的自适应学习.
- 平行神经网络架构用于FOESO的自适应参数学习.
主要成果:
- 据FOESO-Net证明,正如正式分析和证明的那样,它显示了收错误.
- 该方法有效估计接触扭矩,避免复杂的计算,如关节加速.
- 在协作机器人平台上的模拟和实验验证了拟议的方法.
结论:
- 与现有方法相比,FOESO-Net显著减少了估计错误和响应时间.
- 拟议的分数顺序观察器为非线性,快速变化的扭矩观察提供了卓越的性能.
- 这一进步提高了人机交互的安全性和效率.
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