Position learning control for current-fed permanent magnet step motors with uncertainties

We address the state feedback position tracking control problem for uncertain current-fed permanent magnet step motors with non-sinusoidal flux distribution. Under the assumption that the reference profile for the rotor angle is periodic with known period, a robust adaptive learning control algorithm is designed, which "learns" the non-structured unknown periodic disturbance signal due to system uncertainties by identifying the Fourier coefficients of any truncated approximation, while guaranteing L2 and Lx transient performances. It is shown that for any motor initial condition: i) the position tracking error exponentially converges to a residual set which may be arbitrarily reduced by increasing the number of terms in the truncated Fourier series; ii) when the unknown periodic disturbance can be represented by a finite Fourier series, it is exponentially reconstructed by the learning algorithm and exponential position tracking is achieved.