Title :
An optimal neural network speed estimator using genetic algorithms
Author :
Cao, Chengzhi ; Yang, Xiaobo ; Li, Haiping
Author_Institution :
Sch. of Inf. Sci. & Eng., Shenyang Univ. of Technol., China
Abstract :
In direct torque control system, a speed-estimating method based on an optimal neural network using genetic algorithms is presented to estimate the rotor speed of asynchronous motors. According to the equations of asynchronous motors, the equation of speed is gotten, a speed estimator model based on recursive neural networks is also gotten, and genetic algorithms optimize the speed estimator. The simulation result shows the optimized speed estimator can track accurately the variation of speed, and the system has better dynamic performance.
Keywords :
genetic algorithms; induction motors; machine control; neurocontrollers; rotors; torque control; velocity measurement; GA; asynchronous motor rotor speed estimation; direct torque control system; genetic algorithms; optimal neural network speed estimator; recursive neural networks; speed-estimating method; Computer hacking; Equations; Genetic algorithms; Genetic engineering; Information science; Intelligent control; Neural networks; Recursive estimation; Rotors; Torque control;
Conference_Titel :
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN :
0-7803-7268-9
DOI :
10.1109/WCICA.2002.1020063
