在线估计机器人系统计算机扭矩控制的操纵器动力学
1Mediterranean Institute of Technology, Southern Mediterranean University, Tunis 1053, Tunisia.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究引入了一种新的基于数据的机器人控制方法,消除了对精确系统模型的需求. 这种方法通过从运动数据中学习动态来提高机器人系统在不确定的环境中的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 机器学习 机器学习
背景情况:
- 传统的机器人控制需要精确的系统模型,由于现实世界的不确定性,这些模型通常是不切实际的.
- 摩擦和环境相互作用等不可预测的因素限制了基于模型的控制的有效性.
研究的目的:
- 开发一种新的数据驱动框架,用于模拟和估计机器人动态,而无需先前的参数知识.
- 在复杂和不确定的环境中克服传统基于模型的控制的局限性.
主要方法:
- 利用机器人运动期间收集的数据进行动态建模.
- 将经典控制理论与机器学习技术相结合,包括系统识别,物理信息神经网络 (PINNs) 和深度学习.
主要成果:
- 证明了双链路机器人操纵器的优越轨迹跟踪.
- 通过数据驱动方法实现了对未建模动态的增强稳定性.
结论:
- 拟议的统一数据驱动框架有效地模拟了机器人动态,而不需要先前的系统知识.
- 模块化技术可以适应不同数量的关节的机器人操纵器.
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