Application of genetic algorithms in EKF for speed estimation of an induction motor

Author

Cai, Li ; Zhang, Yinhai ; Zhang, Zhongchao ; Liu, Chenyang ; Lu, Zhengyu

Author_Institution

Zhejiang Univ., Hangzhou, China

Volume

1

fYear

2003

fDate

15-19 June 2003

Firstpage

345

Abstract

Genetic algorithm (GA) is applied in this paper to optimize parameters of the extended Kalman filter (EKF) in a speed-senserless field-oriented controller (FOC) system. The main parameters of EKF are the covariance matrics Q and R, which are bound respectively to the state and measurement noises. As for speed-sensorless FOC system, the convergence and precision of both rotor speed and flux estimation depend on the accuracy of the models of system noise and measurement noise, i.e. Q and R. A GA training simulation system of optimum parameters of EKF is given and the simulation results show the efficiency and rationality of the algorithm.

Keywords

Kalman filters; angular velocity control; convergence; genetic algorithms; induction motors; machine theory; machine vector control; magnetic flux; magnetic variables control; rotors; convergence; covariance matrics; extended Kalman filter; genetic algorithms; measurement noise; optimum parameters; rotor flux estimation; rotor speed estimation; speed-senserless field-oriented controller; training simulation system; Control system synthesis; Covariance matrix; Genetic algorithms; Induction motors; Kalman filters; Noise measurement; Q measurement; Robust control; Sensor systems; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics Specialist Conference, 2003. PESC '03. 2003 IEEE 34th Annual

ISSN

0275-9306

Print_ISBN

0-7803-7754-0

Type

conf

DOI

10.1109/PESC.2003.1218317

Filename

1218317