Separable Least Squares Identification of a Parallel Cascade Model of Human Ankle Stiffness

Abstract

The identification of a dynamic nonlinear model of human ankle stiffness is considered in a minimum mean squared error framework. The model consists of two parallel pathways one representing the intrinsic dynamics, the other representing the reflex contribution to the stiffness. The model is shown to be linear in all of its parameters except for those used to describe a single static nonlinearity in the reflex pathway. A separable least squares optimization algorithm is developed which takes advantage of this structure. This new algorithm is applied to experimental stretch reflex data and the results compared to the current state-of-the-art algorithm an iterative technique which fits the two pathways alternately. The relative merits of the two approaches are discussed.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA411939

Entities

People

  • David T. Westwick
  • Robert E. Kearney

Organizations

  • University of Calgary

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Cross Correlation
  • Dynamics
  • Engineering
  • Errors
  • Experimental Data
  • Half-Wave Rectifiers
  • Hydraulic Actuators
  • Identification
  • Linear Systems
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Optimization
  • Polynomials
  • Spinal Cord
  • Stiffness
  • Universities

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