A Robust RF-MRAS based Speed Estimator using Neural Network as a Reference Model for Sensor-less Vector Controlled IM Drives

Abstract

This paper proposes a robust MRAS based speed estimator for sensorless vector controlled IM drives. Rotor Flux based MRAS Model Reference Adaptive System (RF-MRAS) for rotor speed estimation is gaining popularity for its simplicity in sensorless vector controlled IM drives. In this scheme, the voltage model equations are used as the reference model. The voltage model equations in turn depend on stator resistance which varies with temperature during motor operation and more predominant at low frequencies/speed. Hence separate on-line estimator is required to track the stator resistance variation. The newly developed MRAS technique uses a robust Single Neuron Cascaded Neural Network (SNC-NN) based rotor flux estimator trained from input/output data as reference model in the place of the conventional voltage model in RF-MRAS to form a robust RF-MRAS based speed estimator. This makes the reference model robust to stator resistance variation without the need for separate R_s estimator. The performance of the proposed speed estimator is investigated extensively for various operating conditions. The performance of proposed MRAS is shown to work for wide range of operating conditions including zero speed operation. The robustness of the proposed RF-MRAS based speed estimator is demonstrated through MATLAB simulations and compared with the conventional RF-MRAS.

Keywords: Robust Rotor Flux-Model Reference Adaptive System, Rotor flux estimator, neural network, SNC-NN model, Sensor-less operation, vector-controlled IM drives.