A Stable Model Reference Adaptive Controller Developed for a Prosthetic Hand Wrist
Shifa Sulaiman, Paolino De Risi, Francesco Schetter, Fanny Ficuciello
AI summary
Problem
Soft continuum prosthetic wrists suffer from imprecise control due to unmodeled dynamic effects, external disturbances from attached hands, and parametric uncertainties like spring degradation, which traditional controllers cannot compensate in real time.
Approach
The authors develop a Model Reference Adaptive Controller (MRAC) that maps the soft wrist’s dynamics to a reduced-order rigid-joint model, using a novel stability theorem to adapt gains online and reject disturbances without heavy computation.
Key results
- MRAC formulation explicitly channels gravitational disturbances and time-varying spring stiffness into the tracking error for online compensation
- Soft-to-rigid dynamic bridge using RPPR Denavit–Hartenberg parameters enables closed-form inertia and gravity modeling for reduced-order control
- Stability proof for configuration-dependent, time-varying soft-wrist dynamics using the New Theorem of Stability, ensuring error and gain convergence
- Experimental validation on PRISMA HAND II wrist demonstrates fast gain convergence, disturbance rejection, and stable tracking under ±20% stiffness changes and increased payloads
Why it matters
Provides a computationally efficient, theoretically guaranteed control framework that enhances the reliability and dexterity of soft continuum prosthetics in real-world clinical and daily use scenarios.
Abstract
Advanced control algorithms are essential for en- hancing the functionality of prosthetic hands, enabling them to operate in diverse conditions. This paper presents a Model Reference Adaptive Controller (MRAC) developed for a tendon- driven soft continuum wrist, integrated into the ’PRISMA HAND II’ prosthetic hand. The primary objective of our research is to design an adaptive controller that facilitates wrist movements eliminating external disturbances while minimizing computa- tional requirements. To achieve this, kinematic and dynamic models of the wrist are developed based on the Piece-wise Constant Curvature (PCC) hypothesis. The controller consists of a reference model generated using the PCC model, and state errors are evaluated by comparing the responses of the reference model to those of the wrist model. These errors are reduced using the MRAC approach to make the wrist’s behavior closely align with that of the reference model. Stability of the closed-loop system is ensured using the Lyapunov direct method, along with the ’New Theorem of Stability’, a replacement for Barbalat’s lemma, ensuring that the error between the reference model and the actual system converges to zero and that the adaptive gains stabilize to fixed values. The adaptive performance of the controller is evaluated through experimental validations, where the motions of the prosthetic hand attached to the wrist are treated as unknown disturbances, and the mechanical stiffness of the wrist is considered as an uncertain parameter, resulting from the degradation of the internal springs. Note to Practitioners - This study aims to develop an adaptive controller suitable for soft continuum robots operating in diverse environments and conditions. Im- plementation of the proposed controller will enable soft robots to operate effectively in the presence of unknown disturbances.