用神经网络自适应控制假手腕，提升柔顺驱动的精准度。
An Adaptive Neuro-Controller Developed for a Prosthetic Hand Wrist
- 基于神经网络预测肌腱电流，实时补偿腕部形变误差。
- 结合蒂莫申科梁理论建模，实现电机电流到肌腱张力的精确转换。
- 仿真与实验验证有效，适合假肢控制研发人员参考。
假手控制器的重要性不言而喻，它直接决定系统的功能性和可用性。本文针对一种腱驱动的软连续体腕部，提出了一种自适应神经控制器。采用蒂莫申科梁理论建立腕部的运动学与动力学模型，利用神经网络（NN）策略根据腕部偏转误差预测所需的电机电流以调节肌腱。同时，基于蒂莫申科梁理论，由输入电机电流计算出所需肌腱张力。通过与其它类似控制器的对比，分析了所提方法的性能。研究包含仿真与所制腕部的实验验证，证明了该控制器的有效性。
原文摘要 · Abstract (English)
The significance of employing a controller in prosthetic hands cannot be overstated, as it plays a crucial role in enhancing the functionality and usability of these systems. This paper introduces an adaptive neuro-controller specifically developed for a tendon-driven soft continuum wrist of a prosthetic hand. Kinematic and dynamic modeling of the wrist is carried out using the Timoshenko beam theory. A Neural Network (NN) based strategy is adopted to predict the required motor currents to manipulate the wrist tendons from the errors in the deflection of the wrist section. The Timoshenko beam theory is used to compute the required tendon tension from the input motor current. A comparison of the adaptive neuro-controller with other similar controllers is conducted to analyze the performance of the proposed approach. Simulation studies and experimental validations of the fabricated wrist are included to demonstrate the effectiveness of the controller.
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