在联合速度级别的冗余操纵器中,小脑启发的学习和控制方案
IEEE transactions on cybernetics
|August 15, 2024
概括
研究人员开发了一种对冗余操纵器的新型控制方案,其灵感来源于人类小脑. 这种基于回声状态网络 (ESN) 的方法,包括卡尔曼波器,可以在机器人手臂控制中实现精确的轨迹跟踪和噪声耐受性.
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
- 计算神经科学是一种神经科学.
背景情况:
- 冗余操纵器是模仿人类手臂的机械系统,在各种控制应用中至关重要.
- 人类小脑在手臂运动的复杂神经控制中发挥着关键作用.
研究的目的:
- 为学习和控制冗余操纵器设计一个以小脑为灵感的模型.
- 为了提高控制准确性和适用性,使用卡尔曼波器集成的方法.
主要方法:
- 基于回声状态网络 (ESN) 的小脑模型被设计出来.
- 该模型在联合速度水平上实现,以模拟熟练的手臂控制.
- 提出了一个基于ESN的卡尔曼波器结合和小脑启发 (KFICI) 方案.
主要成果:
- 该KFICI方案使冗余操纵器能够在速度水平上跟踪所需的轨迹.
- 拟议的控制方案证明了对噪声的稳定性.
- 模拟和实验验证了KFICI方案在物理冗余操纵器上的有效性.
结论:
- 受小脑启发的ESN模型为冗余操纵控制提供了一种有效的方法.
- 该KFICI计划提高了轨迹跟踪的准确性和噪声耐受性.
- 这种生物启发的控制策略显示了先进机器人应用的巨大潜力.
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