A global robust iterative learning position control for current-fed permanent magnet step motors

We address the tracking control problem via state feedback for uncertain current-fed permanent magnet step motors with non-sinusoidal flux distribution: a periodic reference signal (of known period) for the rotor position is required to be tracked. We design a robust iterative learning control algorithm which, for any motor initial condition and without requiring any resetting procedure, guarantees, despite system uncertainties: exponential convergence of the rotor position tracking error to a residual ball (centered at the origin) whose radius can be made arbitrarily small by properly setting the learning gain; asymptotic convergence of the rotor position tracking error to zero. Robustness with respect to a finite memory implementation of the control algorithm based on the piecewise linear approximation theory is shown to be guaranteed.