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

Shifa Sulaiman, Francesco Schetter, Mehul Menon, Fanny Ficuciello

TL;DR

This work tackles robust, real-time control of a tendon-driven soft continuum wrist integrated into PRISMA HAND II by marrying data-driven learning with model-based control. It develops a PCC-based kinematic/dynamic model and implements a hybrid controller that uses an ANN to predict bending angles from 2D targets and a Sliding Mode Controller to regulate tendon forces, with a tanh-based switching law to suppress chattering. Experimental validation on a fabricated wrist demonstrates strong tracking performance in simulation (RMSE $2.7\times 10^{-4}$ rad, settling time $1.2$ s) and reasonable real-world results (RMSE $0.157$ rad, settling time $3$ s), though external disturbances and spring stiffness variation degrade accuracy. Overall, the approach provides a computation-efficient, robust framework for soft robotic wrists in prosthetic hands, with future work aimed at hardware refinements and real-time sensor integration for enhanced accuracy.$

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.

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

TL;DR

This work tackles robust, real-time control of a tendon-driven soft continuum wrist integrated into PRISMA HAND II by marrying data-driven learning with model-based control. It develops a PCC-based kinematic/dynamic model and implements a hybrid controller that uses an ANN to predict bending angles from 2D targets and a Sliding Mode Controller to regulate tendon forces, with a tanh-based switching law to suppress chattering. Experimental validation on a fabricated wrist demonstrates strong tracking performance in simulation (RMSE rad, settling time s) and reasonable real-world results (RMSE rad, settling time s), though external disturbances and spring stiffness variation degrade accuracy. Overall, the approach provides a computation-efficient, robust framework for soft robotic wrists in prosthetic hands, with future work aimed at hardware refinements and real-time sensor integration for enhanced accuracy.$

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.
Paper Structure (12 sections, 24 equations, 14 figures, 3 tables)

This paper contains 12 sections, 24 equations, 14 figures, 3 tables.

Table of Contents

  1. Introduction
  2. BACKGROUND
  3. DESIGN AND MODELLING OF THE SOFT CONTINUUM WRIST
  4. Design of the Wrist
  5. Kinematic modelling of the wrist using PCC method
  6. Dynamic modelling of the wrist
  7. SLIDING MODE CONTROLLER STRATEGY
  8. RESULTS AND DISCUSSION
  9. Simulation study
  10. Comparison with other controllers
  11. Experimental validation of SMC
  12. CONCLUSION AND FUTURE WORKS

Figures (14)

  • Figure 1: Wrist section (a) Conceptual model (b) Fabricated model
  • Figure 2: Configuration (a) Transformations (b) Single segment (c) RPPR
  • Figure 3: SMC strategy
  • Figure 4: ANN architecture
  • Figure 5: Motion of wrist (a) Radial-1 (b) Radial-2 (c) Ulnar-1 (d) Ulnar-2 (e) Flexion-1 (f) Flexion-2 (g) Extension-1 (h) Extension-2
  • ...and 9 more figures