Robustness analysis of an Extended Kalman Filter for sensorless control of induction motors

2010 ◽  
Author(s):  
F. Alonge ◽  
F. D'Ippolito
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Induction Motors ◽  
Sensorless Control ◽  
Robustness Analysis

scholarly journals An Extended Kalman Filter, with pre-adjusted covariance matrices, applied to the sensorless speed control of three-phase induction motors

2020 ◽  
pp. 109-126
Author(s):  
Leonardo de Magalhães Lopes ◽  
Zélia Myriam Assis Peixoto
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Induction Motors ◽  
Sensorless Control ◽  
Speed Control ◽  
Covariance Matrices ◽  
Operating Conditions ◽  
Control Technique ◽  
State Variables ◽  
Three Phase

With the emergence of sensorless control methods, there was a need for the use of estimators and/or state observers to give it the robustness and precision required in the drive of induction motors. This work deals with the application of the Extended Kalman Filter (EKF) in the estimation of rotor speed and position, aiming at the implementation of the indirect vector control technique in a sensorless speed control system for three-phase induction motors. The mathematical development of the system state variables associated with the EKF stochastic process is presented in this study, and point out its application under variable speed and load conditions, which are imposed on these motors in everyday life. The sensorless control strategy was tested through routine lines in the Matlab® software, simulating operating conditions of this type of engine, being proven its performance, as well as the convergence times consistent with the usual requirements of high performance systems. The main contributions of this work are the use of a reduced-order EKF (ROEKF) and the preset of covariance matrices to accelerate convergence in speed and position estimates for future implementations in currently accessible digital signal processors.

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