Literature DB >> 32280314

Variable Impedance Control of Powered Knee Prostheses Using Human-Inspired Algebraic Curves.

Alireza Mohammadi1, Robert D Gregg2.   

Abstract

Achieving coordinated motion between transfemoral amputee patients and powered prosthetic joints is of paramount importance for powered prostheses control. In this article, we propose employing an algebraic curve representation of nominal human walking data for a powered knee prosthesis controller design. The proposed algebraic curve representation encodes the desired holonomic relationship between the human and the powered prosthetic joints with no dependence on joint velocities. For an impedance model of the knee joint motion driven by the hip angle signal, we create a continuum of equilibria along the gait cycle using a variable impedance scheme. Our variable impedance-based control law, which is designed using the parameter-dependent Lyapunov function framework, realizes the coordinated hip-knee motion with a family of spring and damper behaviors that continuously change along the human-inspired algebraic curve. In order to accommodate variability in the user's hip motion, we propose a computationally efficient radial projection-based algorithm onto the human-inspired algebraic curve in the hip-knee plane.
Copyright © 2019 by ASME.

Entities:  

Year:  2019        PMID: 32280314      PMCID: PMC7104744          DOI: 10.1115/1.4043002

Source DB:  PubMed          Journal:  J Comput Nonlinear Dyn        ISSN: 1555-1415


  3 in total

1.  Nonholonomic Virtual Constraints for Control of Powered Prostheses Across Walking Speeds.

Authors:  Jonathan C Horn; Robert D Gregg
Journal:  IEEE Trans Control Syst Technol       Date:  2021-12-21       Impact factor: 5.418

Review 2.  Variable Impedance Control and Learning-A Review.

Authors:  Fares J Abu-Dakka; Matteo Saveriano
Journal:  Front Robot AI       Date:  2020-12-21

3.  Control Framework for Sloped Walking With a Powered Transfemoral Prosthesis.

Authors:  Namita Anil Kumar; Shawanee Patrick; Woolim Hong; Pilwon Hur
Journal:  Front Neurorobot       Date:  2022-01-11       Impact factor: 2.650

  3 in total

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