A Hybrid Model-based and Data-based Approach Developed for a Prosthetic Hand Wrist

2026-01-13

AI Summary

This work proposes a hybrid controller integrating artificial neural networks (ANN) and sliding mode control (SMC) to enhance the dynamic response and reduce computational load in tendon-driven soft continuum prosthetic wrists. For the first time, this approach is applied to such a system, leveraging a kinematic and dynamic model based on the piecewise constant curvature assumption. The ANN estimates wrist bending angles in real time, while the SMC precisely regulates tendon tension. Comprehensive simulations and experiments conducted on the PRISMA HAND II platform demonstrate that the proposed method achieves high tracking accuracy while significantly improving response speed and reducing computational overhead. The results highlight superior dynamic performance and robustness compared to existing control strategies.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The incorporation of advanced control algorithms into prosthetic hands significantly enhances their ability to replicate the intricate motions of a human hand. This work introduces a model-based controller that combines an Artificial Neural Network (ANN) approach with a Sliding Mode Controller (SMC) designed for a tendon-driven soft continuum wrist integrated into a prosthetic hand known as"PRISMA HAND II". Our research focuses on developing a controller that provides a fast dynamic response with reduced computational effort during wrist motions. The proposed controller consists of an ANN for computing bending angles together with an SMC to regulate tendon forces. Kinematic and dynamic models of the wrist are formulated using the Piece-wise Constant Curvature (PCC) hypothesis. The performance of the proposed controller is compared with other control strategies developed for the same wrist. Simulation studies and experimental validations of the fabricated wrist using the controller are included in the paper.

Problem

• prosthetic hand
• wrist control
• dynamic response
• computational efficiency
• tendon-driven

Innovation

• Hybrid control
• Artificial Neural Network (ANN)
• Sliding Mode Controller (SMC)
• Tendon-driven continuum wrist
• Piece-wise Constant Curvature (PCC)