Optimizing Prosthetic Wrist Movement: A Model Predictive Control Approach

TL;DR

This paper demonstrates that Model Predictive Control (MPC) can significantly enhance the dexterity and responsiveness of prosthetic wrists, enabling more natural and intuitive movements through predictive modeling and experimental validation.

Contribution

It introduces an MPC-based control strategy for prosthetic wrists that reduces computational effort and improves movement precision and user interaction.

Findings

MPC improves prosthetic wrist dexterity.

Enhanced responsiveness and natural movement in prosthetic control.

Validated through simulation and experimental tests.

Abstract

The integration of advanced control strategies into prosthetic hands is essential to improve their adaptability and performance. In this study, we present an implementation of a Model Predictive Control (MPC) strategy to regulate the motions of a soft continuum wrist section attached to a tendon-driven prosthetic hand with less computational effort. MPC plays a crucial role in enhancing the functionality and responsiveness of prosthetic hands. By leveraging predictive modeling, this approach enables precise movement adjustments while accounting for dynamic user interactions. This advanced control strategy allows for the anticipation of future movements and adjustments based on the current state of the prosthetic device and the intentions of the user. Kinematic and dynamic modelings are performed using Euler-Bernoulli beam and Lagrange methods respectively. Through simulation and experimental validations, we demonstrate the effectiveness of MPC in optimizing wrist articulation and user control. Our findings suggest that this technique significantly improves the prosthetic hand dexterity, making movements more natural and intuitive. This research contributes to the field of robotics and biomedical engineering by offering a promising direction for intelligent prosthetic systems.