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基于隐藏表示的学习控制器用于压力驱动软执行器的气动和液压双驱动

Taku Sugiyama¹, Kyo Kutsuzawa¹, Dai Owaki¹

¹Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.

|August 17, 2023

概括

一个新的LAtent基于表示的Feedforward神经网络 (LAR-FNN) 能够使用气动或液压执行来准确控制软机器人. 这种方法独立的方法简化了双启动系统的开发.

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
人工智能的人工智能

背景情况:

压力驱动软执行器 (PSA) 为新型软机器人提供了潜力,但由于流体特性不同,在双重气动和液压执行控制方面面临着挑战.
通过空气和水操作来实现PSA的准确,方法独立的控制是很困难的,这阻碍了实际应用.

研究的目的:

开发一种基于LAtent表示的新Feedforward神经网络 (LAR-FNN),用于实现软机器人的双重气动和液压驱动系统的准确和适应性控制.
为了证明LAR-FNN能够在很短的适应时间内提供方法独立的控制性能.

主要方法:

拟议的LAR-FNN集成了一个自动编码器 (AE) 来从PSA的楼梯响应中生成一个潜在的表示.
一个前神经网络 (FNN) 使用这种潜伏表示来创建一个针对目标PSA的个别反向模型,并计算前控制输入.

主要成果:

使用PSA的实验结果证实,LAR-FNN可以实现准确的控制性能,不管使用的是哪种操作方法 (气动或液压).
LAR-FNN 证明了较短的适应时间,满足单个神经网络的双动作控制的要求.

结论:

LAR-FNN为双驱软机器人提供了统一的控制方法,克服了不同气动和液压特性所带来的局限性.
这项研究有助于通过改进的控制策略来推进软双驱动机器人开发和软机器人技术的更广泛领域.

关键词:

推进神经网络的前进料个体变形补偿的个人变形补偿.代学习控制代学习控制压力驱动的软执行器.软机器人软机器人软机器人轨迹跟踪轨迹跟踪轨迹跟踪

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