Learning Control of Current-fed Induction Motor with Mechanical Uncertainties

In this paper repetitive learning control technique has been applied to the position/flux tracking control of an Induction Motor (IM) under hypothesis of periodic reference trajectory and uncertainties on the mechanical model. The electro-magnetic IM model has been directly taken into account in the control development. Indirect Field Oriented approach has been exploited and combined with control actions derived from Lyapunov-like design. In order to compensate the periodic disturbances, the model of a generic periodic signal with known period has been embedded in the controller with a suitable update rule. The convergence properties of the overall solution proposed have been formally proven. Simulation results confirm the validity of the approach presented. Copyright 2005 IFAC