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A Discrete-Time Nonlinear Estimator for an Orthosis-Aided Gait

[+] Author Affiliations
Ashwin Dani

University of Connecticut, Storrs, CT

Nitin Sharma

University of Pittsburgh, Pittsburgh, PA

Paper No. DSCC2014-6161, pp. V001T04A003; 9 pages
doi:10.1115/DSCC2014-6161
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4618-6
  • Copyright © 2014 by ASME

abstract

To achieve automatic operation of a powered orthosis-aided gait or functional electrical stimulation-based walking restoration, accurate estimation of the leg angles is of utmost importance. Various phases of walking last for a short duration of time; thus, an accurate estimator is required with a fast convergence rate. To overcome this challenge, this paper presents a discrete-time nonlinear estimation algorithm to estimate lower-limb angles during an orthosis-aided walking. To this end, we use measurements from 6 degree-of-freedom (DOF) inertial measurement units (IMUs) to estimate the lower limb angles. The estimator is based on a state-dependent coefficient (SDC) linearization or extended linearization of the nonlinear functions. A combination of multiple discrete SDCs is used to compute an optimal gain of the nonlinear estimator based on uncertainty minimization criteria. The nonlinear estimator is robust to uncertainties in system modeling and sensor noise/bias from the IMUs. Monte Carlo simulation studies reveal that the estimator outperforms widely used discrete-time extended Kalman (EKF) filter with respect to average root-mean squared estimation error (RMSE) criteria.

Copyright © 2014 by ASME
Topics: Orthotics

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