对于使用不够信息的重腿机器人,执行伺服扭矩故障诊断
Shaoxun Liu1, Shiyu Zhou1, Boyuan Li1
1Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dongchuan RD. Minhang District, Shanghai, 200240, China.
ISA transactions
|February 13, 2024
概括
本研究提出了一种新的方法来诊断重腿机器人 (HLR) 中的伺服器故障,仅使用关节位置和运动数据,减少对脆弱传感器的依赖. 该框架允许实时故障诊断,即使没有直接的伺服器输出测量.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机械工程 机械工程
- 控制系统 控制系统
背景情况:
- 重腿机器人 (HLR) 的伺服器故障对于可靠性至关重要.
- 传统的诊断依赖于脆弱的传感器,限制了强度.
- 机械磨损,环境因素和电气问题导致伺服器故障.
研究的目的:
- 为HLR伺服故障诊断开发一个独立于传感器的框架.
- 为了减轻对诊断系统中微妙传感器的依赖.
- 为了实现实时伺服扭矩故障诊断.
主要方法:
- 使用关节位置和永磁同步电机 (PMSM) 信息.
- 开发了一个连接PMSM电流与HLR运动的模型.
- 对未测量的输出实施改进的滑动模式观察器 (ISMO).
- 采用富里埃膨胀模型进行无故障干扰的表征.
- 使用双线颗粒过器 (DPF) 进行干扰预测.
- 将DPF输出集成为ISMO的前,用于实时诊断.
主要成果:
- 为HLR伺服故障诊断建立了一个新的框架.
- 拟议的方法有效地使用有限的传感器数据诊断伺服器故障.
- 即使服务器输出不可用,也可以实现实时故障诊断.
- 模拟和实验验证了框架的准确性和有效性.
结论:
- 开发的框架通过减少对传感器的依赖来提高HLR诊断可靠性.
- 集成PMSM数据,ISMO和DPF,为伺服故障检测提供了一个强大的解决方案.
- 这种方法为现实世界的机器人应用提供了一种实用且经过验证的方法.
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