Repetitive learning position control for full order model permanent magnet step motors

We provide a novel theoretical solution to the yet unsolved problem of tracking, via state feedback, periodic reference signals (with known period) for the rotor position of full order model uncertain permanent magnet step motors with non-sinusoidal flux distribution and uncertain position-dependent load torque. The resulting control is of simple structure and incorporates three repetitive learning estimation schemes generalizing the classical integral actions. Realistic simulation results illustrate the effectiveness of the proposed approach.