一种基于系统的增量广泛学习方法,用于用于机器人远程操作的震动减弱
Guanyu Lai1, Weizhen Liu1, Weijun Yang2
1School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种基于广泛学习系统的新型震过器,以消除远程操作系统中的手震. 这种新方法为精确的机器人任务提供了快速学习和高精度,例如远程手术.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 物理手阻碍了高精度远程操作任务,如远程手术.
- 现有的方法,如深度学习,由于超参数而面临缓慢的训练,而支持矢量机器方法可能会牺牲准确性.
研究的目的:
- 为远程操作系统提出和评估一种新的震过器,它克服了现有方法的局限性.
- 在高精度机器人应用中,以高精度实现快速的消除.
主要方法:
- 开发了一种基于广泛学习系统的震过器,集成增量学习算法.
- 使用滑动模式控制方法来提高闭环系统的控制性能.
- 该系统利用增量学习进行持续的参数更新,并在错误趋同时停止.
主要成果:
- 模拟实验证明了拟议的基于广泛学习系统的震过器的可行性和有效性.
- 过器表现出快速的学习速度,并由于其独特的网络结构而保持准确性.
- 集成的滑动模式控制提高了奴隶机器人系统的整体性能.
结论:
- 提出的基于广泛学习系统的震过器是缓解远程操作中的生理手的可行解决方案.
- 这种方法在实现精确和稳定的机器人操作方面取得了重大进展,特别是在像远程手术这样的敏感领域.
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