Symbiotic Organism Search of a NonLinear Kalman Filter for Induction Motors Sensor-less Control

In this paper a new strategy presents for achieving a high performance estimation for controlling the sensorless induction Motors presents. The suggested technique uses the Symbiotic Organism Search Optimization (SOS) algorithm to tune the Extended Kalman Filter observer through evaluating noise matrix (R) and resolving the optimal covariance matrix (Q) values. The overall idea is to construct optimized Extended Kalman Filter observer with the basic one. Results of the simulation verified the efficacy in terms of accuracy and adaptive performance of the proposed strategy.