Sensorless Direct Field-Oriented Control of Induction Motor Drive Using Artificial Neural Network-Based Reactive Power MRAS

In this paper, advanced sensorless control methods of an induction machine (IM) with the use of a model reference adaptive system (MRAS) estimator are presented, specifically an MRAS using reactive power (Q-MRAS) with an implemented feedforward artificial neural network. Advantages of the proposed solution in comparison with the conventional Q-MRAS include better robustness, less dependence on the IM parameters, and stable operation in the regenerative mode. The simulations were performed in the MATLAB Simulink interface to validate the proposed approach. The algorithm was implemented in a single-core TMS320F28335 real-time Digital Signal Controller with a LabVIEW control panel. The experimental results were obtained on a three-phase experimental induction motor drive with a nominal power of 2.2 kW.