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 L_x 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.