Schauer T., N.O. Negard, F. Previdi, J. Raisch, K.J. Hunt
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.
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