A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under non-isometric 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 shankquadriceps 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 Preprint submitted to Control Engineering Practice 16 July 2004explicitly designed whereas the model was identified a priori using the developed identification procedure.

Monotonic convergence of linear iterative learning control (ILC) systems with changing pass length is considered. The maximum pass length (MPL) error is introduced as a useful concept for convergence analysis of this class of systems. Using the lifted-system framework, a both necessary and sufficient monotonic convergence criterion is found for the 1-norm of the MPL error. Further findings on 2-norm and ∞-norm convergence are added. Finally, an example system is given, i.e. the control of an electrical stimulation system for gait assistance, and simulation results are provided.