Human motor control: Learning to control a time-varying, nonlinear, many-to-one system
Human motor control: Learning to control a time-varying, nonlinear, many-to-one system
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DOI：
10.1109/5326.827449
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发表时间：
2000-02-01
期刊：
影响因子：
--
通讯作者：
Inbar, GF
中科院分区：
文献类型：
--
作者：
Karniel, A;Inbar, GF
人类运动控制一直是科学家和工程师面临的巨大挑战。它提出了他们发现难以处理和操纵的大多数问题，这是因为它是一个分布式、非线性、时变系统，具有多个自由度，包括许多层次的冗余。近年来，计算机的快速发展和神经计算这一新的科学领域的出现，使得在人体运动控制性能的建模中考虑复杂的、自适应的、并行的体系结构成为可能。在本文中，一些模型已被用于电机控制的研究进行了审查，并提出了一些开放的问题进行了形式化和讨论。主要议题是自适应和人工神经网络控制，参数估计，肌肉的非线性特性，并行和冗余。
Human motor control has always presented a great challenge to both scientists and engineers. It has presented most of the problems they have found difficult to handle and manipulate, which is a consequence of it being a distributed, nonlinear, time-varying system with multiple degrees of freedom that include redundancy on many levels. In recent years, the fast development of computers and the emergence of the new scientific field of neural computation have enabled consideration of complex, adaptive, parallel architectures in the modeling of human motor-control performance. In this paper, some of the models that have been used in the study of motor control are reviewed, and some open questions are formalized and discussed. The main topics are adaptive and artificial neural-networks control, parameters estimation, nonlinear properties of the muscles, and parallelism and redundancy.