A feedforward neural network controlling the movement of a 3-DOF finger
A feedforward neural network controlling the movement of a 3-DOF finger
复制标题
控制三自由度手指运动的前馈神经网络
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
G. Magenes
中科院分区：
文献类型：
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作者：
E. Secco;G. Magenes
本文描述了一种三自由度振铃器的动态控制，该振铃器模仿人的手指在空间中到达期望的指尖位置。该控制器由一个神经网络(NN)组成，它向指骨提供必要的三个力矩，从而使指尖运动具有流畅的“自然”速度轮廓。为了得到结果，我们面临以下问题：1)消除第三关节的冗余度；2)自然运动的数学描述；3)执行运动的力矩的计算；4)神经网络结构的优化。假设文献中已经确立了一种“认知”的方法，那么第一点和第二点将通过采用最小急流理论的扩展来解决。采用经典的拉格朗日方程计算三电机的转矩。最后，训练多层感知器(MLP)神经网络以自然方式移动设备。在新的未知运动上检验了神经网络的泛化能力，并根据代表电机性能的指标比较了不同的MLP结构。结果表明，为了实现自然的神经网络假肢/机器人动态控制系统，我们应该对多指手进行这种方法的研究。
This paper describes the dynamic control of a 3 degree of freedom (DOF) ringer emulating a human finger for reaching a desired fingertip position in space. The control consists of a neural network (NN) which provides the necessary three torques to the phalanges granting a smooth "natural" speed profile of the fingertip motion. To obtain the results, we face these problems: 1) the elimination of the redundancy due to the third joint; 2) the mathematical description of a natural movement; 3) the calculation of the torques for executing movement; and 4) the optimization of the NN's structure. Assuming a "cognitive" approach well-established in the literature, the first and the second points are solved by adopting an extension of the minimum jerk theory. The classic Lagrange equations are applied to compute the three-motor torque. Finally, a multilayer perceptron (MLP) NN is trained to move the device in a natural manner. The generalization capabilities of the NN are checked on new never-seen movements, and different MLP architectures are compared on the basis of indexes representing the motor performance. The results suggest we should pursue this approach for multifinger hand in order to achieve a natural NN prosthetic/robotic dynamic control system.
影响因子：
2.5
作者：
SAINBURG, RL;POIZNER, H;GHEZ, C
通讯作者：
GHEZ, C