基于RBFNN和梯度下降的操纵器的可变参数阻抗控制
Linshen Li1,2,3, Fan Wang1,3, Huilin Tang1,2,3
1Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了用于机器人操纵器的新型变量参数阻抗控制,提高了掌握任务的精度. 该方法使用辐射基函数神经网络和梯度下降,减少计算负载并适应对象的刚性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 精确的力和位置控制对于操纵器抓取任务至关重要,以防止对象损坏.
- 现有的混合力/位置控制方法是计算密集的.
- 对于与不同硬度的物体相互作用的操纵器,需要适应性控制.
研究的目的:
- 为操纵器提出一个计算效率高的可变参数阻抗控制方法.
- 在抓取过程中实现并发和精确的力和位置控制.
- 为了减少手动阻抗参数调节的需要.
主要方法:
- 一个基于位置的阻抗控制结构,集成代学习控制和梯度下降.
- 滑动模式控制器的设计,以处理系统的不确定性和干扰.
- 使用一个辐射基函数神经网络 (RBFNN) 进行系统识别.
- 通过梯度下降方法反复调整阻抗参数.
主要成果:
- 拟议的方法在操纵器抓取任务中实现了精确的力和位置控制.
- 通过模拟和实验验证证明了有效性.
- 成功地适应了具有不同刚度的物体,而无需手动参数调整.
结论:
- 变量参数阻抗控制方法为传统方法提供了优越的替代方案.
- 整合RBFNN和梯度下降显著提高了控制精度和适应性.
- 这种方法提高了机器人操纵任务的安全性和效率.
关键词:
在RBFNNNNNNNNNN梯度下降的降落方式阻抗控制控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制操纵者 操纵者 是一个操纵者.更多相关视频
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