An MRAS Based Estimation Method with Artificial Neural Networks for High Performance Induction Motor Drives and its Experimentation

K. Negadi(1*), A. Mansouri(2), F. Marignetti(3), M. Touam(4)

(1) Energy and Smart Systems Laboratory (LESI), Khemis Miliana University, Algeria
(2) Laboratory of Automatics and Systems Analysis (L.A.A.S.), Department of Electrical Engineering, Algeria
(3) Dipartimento di Automazione Università degli Studi di Cassino, Italy
(4) Laboratory of Automatics and Systems Analysis (L.A.A.S.), Department of Electrical Engineering, E.N.P. Oran BP 1523 El’ M’naouer, Oran, Algeria., Algeria
(*) Corresponding author

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This paper presents an adaptive speed observer for an induction motor using an artificial neural network with a direct field-oriented control drive. The speed and rotor flux are estimated with the only assumption that from stator voltages and currents are measurable. The estimation algorithm uses a state observer combined with an intelligent adaptive mechanism based on an artificial neural network (ANN) to estimate rotor speed. The speed is estimated by a simple Proportional-Integrator (PI) controller, which reduces sensitivity to variations, due essentially to the influence of temperature. The proposed sensorless control scheme is tested for various operating conditions of the induction motor drive. Simulation and experimental results demonstrate a good robustness against load torque disturbances, the estimated components of the stator currents and rotor speed converge to their true values, which guarantees that a precise trajectory tracking with the prescribed dynamics.
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Field Oriented Control; Induction Motor Drive; Artificial Neural Network; MRAS Based Estimation; Sensorless Drive

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