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Human Motor Control and the Internal Model Principle * * The authors wish to acknowledge the financial support of the National Science Foundation under award number 1035271.

机译:人体运动控制和内部模型原理 * * 作者希望感谢国民党的财政支持科学基金会(Science Foundation),授予编号1035271。

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Abstract: In the motor behavior literature, the observation that humans can learn and adapt to altered limb dynamics is often explained with reference to internal models. Forward internal models enable robust performance even when feedback is intermittent or delayed while inverse models simplify the loop dynamics to manageable form. Internal models of coupled limb/object dynamics, however, do not address the skill with which humans track references or reject disturbances that can be recognized and anticipated. We propose that internal models of reference or disturbance signals create a useful framework for addressing open questions in human motor behavior. We appeal to the Internal Model Principle in control theory, which suggests that a model of the predictable signal (a signal generator) belongs in the controller. When the control loop is already stable, such an internal model will produce perfect tracking or disturbance rejection in steady state. In this paper we apply the Internal Model Principle to hypothesize controllers that elicit cyclic behaviors from systems that feature elastic and inertial energy storage. We also outfit this model with features that describe human neuromuscular dynamics and thereby enable the description of two roles for force feedback: carrying power that couples the human/machine dynamics and carrying information for neural control processing.
机译:摘要:在运动行为文献中,经常参考内部模型来解释人类可以学习并适应肢体动力学变化的观察。正向内部模型即使在反馈是间歇性的或延迟的情况下也能提供强大的性能,而反向模型则将环路动力学简化为易于管理的形式。但是,肢体/物体动力学耦合的内部模型不能解决人类跟踪参考或拒绝可以识别和预期的干扰的技能。我们建议参考或干扰信号的内部模型创建一个有用的框架,以解决人类运动行为中的开放性问题。我们呼吁控制理论采用内部模型原理,该理论表明可预测信号的模型(信号发生器)属于控制器。当控制回路已经稳定时,这种内部模型将在稳态下产生完美的跟踪或干扰抑制。在本文中,我们将内部模型原理应用于假设控制器,这些控制器从具有弹性和惯性能量存储的系统中引出循环行​​为。我们还为该模型配备了描述人类神经肌肉动力学的功能,从而能够描述力反馈的两个角色:承载人与机器动力学耦合的动力以及承载神经控制处理的信息。

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