Adaptive learning control for induction motor servo drives

The output feedback tracking control problem for 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, while guaranteing L-2 and L-infinity transient performances. It is shown that, for any motor initial condition belonging to an arbitrary given compact set: i) the guaranteed output tracking precision improves 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 output tracking is achieved. Simulation results for a digital implementation of the proposed controller are provided.