Abstract
It is well known that the performance of the (industry standard) fieldβoriented control (FOC) for induction motors is highly sensitive to uncertainties in the rotor resistance. In this paper we describe how to use supervisory control to obtain an adaptive implementation of FOC for currentβfed machines. The unknown rotor resistance is assumed to belong to a discrete set, while the uncertain load torque ranges in a given compact set. Even though no restrictions are a priori imposed on the size of these sets, their definitions reflect the prior knowledge of the designer, which is effectively incorporated in the supervisory control algorithm. The supervisor selects from these sets values for the parameters to be applied to the FOC, a choice that is made by continuously comparing suitably defined performance signals. We prove that the proposed supervisor achieves global stabilization of the system when the load torque is known to belong to a given finite set of values. Apparently, this is the first globally convergent adaptive algorithm for currentβfed machines which simply adds adaptation to the widely popular FOC and is not a radically new complicated controller, hence it is more likely to be adopted by practitioners. Some simulation results illustrate the properties of the algorithm. Copyright Β© 2001 John Wiley & Sons, Ltd.