多变量代学习控制设计用于灵活料驱动器的精密控制
Yulin Wang1,2, Tesheng Hsiao1
1Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了CNC机床的多变量代学习控制 (MILC). 该方法通过使用电机和表反来提高动态定位准确性,提高灵活料驱动系统的精度.
科学领域:
- 机械工程 机械工程
- 控制系统工程 控制系统工程
背景情况:
- 现代加工需要更高的速度和精度,推动了对数控机床中先进控制系统的需求.
- 传统的CNC控制器使用电机侧旋转编码器进行位置反,以确保稳定性并防止元件损坏,但局限性仍然存在.
- 诸如误差,振动和热变形等因素会影响灵活料驱动系统的定位准确性.
研究的目的:
- 为灵活的数控料驱动系统开发和验证一种新的多变量代学习控制 (MILC) 方法.
- 通过整合来自电机和桌面两侧的反来提高动态定位的准确性.
- 为了减轻控制冲突,减少灵活结构中的跟踪错误.
主要方法:
- 实施一个多变量代学习控制 (MILC) 策略.
- 利用来自电机和桌面两侧的错误数据来提高精度.
- 通过标准优化将补偿命令注入参考轨迹和控制命令.
- 在工业双轴数控机床上进行实验验证.
主要成果:
- MILC方法有效地提高了灵活料驱动系统的动态定位精度.
- 整合双侧反 (电机和桌面) 导致更高的精度.
- 反控制 (FBC) 和传统的代学习控制 (ILC) 之间的冲突得到了缓解.
- 在桌面侧实现了显著较小的跟踪错误.
结论:
- 拟议的MILC方法为提高CNC机床精度提供了强大的解决方案.
- 这种方法显示了在高速,高精度加工中推进控制策略的巨大潜力.
- 实验验证证证实了MILC在工业应用中的有效性.
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