Global Adaptive Learning Control for Current-fed Induction Motor Servo Drives

The problem of designing a global output feedback tracking control for current-fed induction motor servo drives with mechanical uncertainties is addressed. Under the assumption that the reference profile for the rotor angle is periodic with known period, an adaptive learning control is designed which "learns" the non-structured unknown periodic disturbance signal due to mechanical uncertainties, by identifying the Fourier coefficients of any truncated approximation. It is shown that the output tracking precision improves by increasing the number of terms in the truncated Fourier series; when the unknown periodic disturbance can be represented by a finite Fourier series, it is asymptotically reconstructed by the learning algorithm and exponential output tracking is guaranteed. L₂ and L_infin transient performances for the output tracking error are guaranteed in the learning phase