Real-Time Implementation of Extended Kalman Filter Observer With Improved Speed Estimation for Sensorless Control

Abstract

This work presents an investigation on Improved Extended Kalman Filter (IEKF) performance for induction motor drive without a speed sensor. The performance of a direct sensorless vector-controlled system through simulation and experimental work is tested. The proposed observer focuses on estimating rotor flux and mechanical speed of rotor from the stationary axis components. Extended Kalman Filters’ estimation performance depends on the system matrix’s proper value ( $Q$ ) and measurement error matrix ( $R$ ). These matrices are assumed to be persistent and are calculated by the trial-and-error method. But, the operating environment affects these matrix values. They must be updated based on the prevailing operating conditions to get high speed and accurate estimates. The values of Q and R in the Improved EKF (IEKF) algorithm are obtained using the genetic algorithm. Besides, IEKF is incorporated to reduce in computational burden for real-time applications.

Keywords

• Extended Kalman filter
• inductor motor
• real-coded-genetic algorithm
• sensorless drives
• speed estimator