Load torque and stator resistance estimations with unscented Kalman filter for speed-sensorless control of induction motors

2017 ◽  
Author(s):  
Recep Yildiz ◽  
Murat Barut ◽  
Emrah Zerdali ◽  
Remzi Inan ◽  
Ridvan Demir
Keyword(s):  
Kalman Filter ◽  
Induction Motors ◽  
Unscented Kalman Filter ◽  
Sensorless Control ◽  
Speed Sensorless ◽  
Load Torque ◽  
Stator Resistance

scholarly journals Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

2018 ◽  
Vol 3 (1) ◽  
pp. 115-127 ◽  
Author(s):  
Emrah Zerdali ◽  
Murat Barut
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Induction Motors ◽  
Viscous Friction ◽  
Speed Sensorless ◽  
Load Torque ◽  
Observability Analysis ◽  
Friction Term ◽  
Flux Estimation

Abstract This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

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