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Online identification and nonlinear control of the electrically stimulated quadriceps muscle

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Schauer,  T.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Centre for Rehabilitation Engineering, University of Glasgow, Glasgow G12 8QQ, Scotland, UK;

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Negard,  N.-O.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Ferchland,  E.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Raisch,  J.
Systems and Control Theory, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Citation

Schauer, T., Negard, N.-O., Previdi, F., Hunt, K. J., Fraser, M. H., Ferchland, E., et al. (2005). Online identification and nonlinear control of the electrically stimulated quadriceps muscle. Control Engineering Practice, 13(9), 1207-1219. doi:10.1016/j.conengprac.2004.10.006.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-9CD0-3
Abstract
A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under nonisometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth-active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shank-quadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is explicitly designed whereas the model was identified a priori using the developed identification procedure. Copyright © 2004 Elsevier Ltd. All rights reserved. [accessed February 8th 2013]