Robust Output Feedback Learning Control for Induction Motor Servo Drives

This paper addresses the output feedback tracking control problem for induction motor servo drives with mechanical uncertainties. Under the assumption that the reference profile for the rotor angle is periodic with known period, a robust adaptive learning control 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 guaranteeing L2 and L1 transient performances. It is shown that, for any motor initial condition belonging to an arbitrary given compact set, by properly setting the control parameters: i) the position and flux modulus tracking errors exponentially converge to residual sets 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, the position and flux modulus tracking errors exponentially converge to zero.