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: rotor angle, rotor speed and stator Currents are assumed to be available for feedback. A robust adaptive learning control is designed under the assumption that the reference profile for the rotor angle is periodic with known period: it 'learns' the periodic disturbance signal by identifying the Fourier coefficients of any truncated approximation; L-2 and L-infinity transient performances are guaranteed in the 'learning phase'. It is shown that, for any motor initial condition belonging to an arbitrary given compact set, by properly setting the control parameters: (i) the rotor 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 rotor position and flux modulus tracking errors exponentially converge to zero. As in field oriented-control, the control algorithm generates references for the magnetizing flux component and for the torque component of the stator current leading to significant simplifications for current-fed motors. Copyright (C) 2008 John Wiley & Sons, Ltd.