An Adaptive Neuro-Controller Developed for a Prosthetic Hand Wrist

TL;DR

This paper presents an adaptive neuro-controller for a prosthetic hand's wrist, utilizing neural networks and Timoshenko beam theory for improved control and validation through simulations and experiments.

Contribution

It introduces a novel neuro-controller for a tendon-driven prosthetic wrist, integrating kinematic modeling and neural prediction for enhanced performance.

Findings

The neuro-controller accurately predicts motor currents from wrist deflections.

Simulation and experimental results show improved control over existing methods.

The approach effectively manages tendon tension using Timoshenko beam theory.

Abstract

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.