A new high performance intelligent speed controller for induction motor based on supervisory loops

A novel high performance intelligent controller for precise speed control of Induction Motor (IM) based on supervisory loop principle is proposed. The scheme consists of online growing Radial Basis Neural Network (RBFNN) controllers working in parallel with an implicit model adaptive controller based on system supervision. The proposed intelligent supervisory loop control architecture monitors system movement and compensate for drastic changes in system parameters and model errors. The main advantage of this unique architecture based on vector control is that, it is precise, feasible and more effective especially when the IM is subjected to unknown dynamics. Simulation results are presented in order to show that, under model errors and drastic changes, the tracking errors are reduced while using the proposed hybrid control scheme at various IM operating conditions.