Optimizing Prosthetic Wrist Movement: A Model Predictive Control Approach

2025-10-22

AI Summary

To address the limited naturalness and responsiveness of tendon-driven prosthetic wrists, this paper proposes a computationally efficient model predictive control (MPC) framework enabling real-time, user-intent-driven adaptive regulation. The method innovatively integrates Euler–Bernoulli beam theory to formulate a kinematic model and employs Lagrangian mechanics to derive a dynamics model—balancing modeling fidelity with computational tractability for embedded implementation. Comprehensive simulation and experimental validation demonstrate substantial improvements in wrist joint trajectory tracking accuracy and dynamic response speed: operational error decreases by 32%, and subjective naturalness ratings increase by 41%. This work establishes an embeddable, lightweight, and highly adaptive control solution for intelligent prosthetic wrists, advancing practical deployment of responsive, user-centered upper-limb prostheses.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizing prosthetic wrist movement using Model Predictive Control
• Reducing computational effort in tendon-driven prosthetic hands
• Enhancing dexterity through predictive modeling of user intentions

Innovation

• Model Predictive Control for prosthetic wrist movement
• Euler-Bernoulli beam and Lagrange modeling methods
• Predictive modeling enables precise movement adjustments