Artificial Intelligence-Based Sensorless Control of Induction Motor with Dual-Field-Orientation

This paper introduces a speed sensorless Dual Field-Oriented Control (DFOC) strategy for induction motors (IM). DFO combines the advantages or rotor-and stator-field orienta-tion to significantly reduce the parameter sensitivity of the control regarding the generation of the converter control variable. A simplified structure is also proposed, using only two regulators for the flux and speed control, eliminating the two current regulators. Related to sensorless control, the classical adaptation mechanism within the MRAS (Model Reference Adaptive System) observer has been replaced with artificial intelligence (AI)-based approaches. Specifically, artificial neural networks (ANN) and recurrent neural networks (RNN) are employed for rotor speed estimation. The effectiveness of the proposed sensor-less control scheme is validated through both simulation and real-time implementation. The results demonstrate that ANN- and RNN-based observers, as deep learning models, provide reliable and accurate rotor speed estimation under various operating conditions.