基于非线性干扰观察员和神经网络的神经网络的断裂减小机器人的力量/位置跟踪控制
Jintao Lei1, Zhuangzhuang Wang1
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
概括
这项研究引入了破裂减少机器人的新型控制方法,显著提高了位置跟踪准确性和合规性. 增强的机器人安全性和精度满足了关键的外科手术要求.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 神经网络的神经网络的神经网络
- 控制系统 控制系统
背景情况:
- 断裂减少机器人面临的挑战是动态负载,模型不确定性和影响准确性和合规性的干扰.
- 肌肉伸展和内部/外部干扰会影响机器人手术工具的性能.
研究的目的:
- 为了提高位置跟踪精度和破裂减少机器人的合规性.
- 为了提高机器人辅助骨折减少手术的安全性和可靠性.
主要方法:
- 开发了一种集成辐射基函数神经网络 (RBFNN) 与非线性干扰观察器的控制方法.
- 进入控制被用于力量跟踪,以提高机器人的合规性和安全性.
主要成果:
- 位置跟踪误差小于±0.2毫米.
- 角位移误差小于±0.3°. 这一误差为±0.3°.
- 最大力追踪误差为26.28N,符合外科标准.
结论:
- 拟议的控制方法显著提高了机器人辅助骨折减少的安全性和准确性.
- 集成的RBFNN和非线性干扰观察员方法显示了手术机器人技术的前景.
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