Title :
Adaptive vector control of a three-phase induction motor using neural networks
Author :
Von Zuben, Fernando J. ; Netto, Márcio L A ; Bim, Edson ; Szajner, Jaime
Author_Institution :
Sch. of Electr. Eng., Univ. Estadual de Campinas, Sao Paulo, Brazil
fDate :
27 Jun- 2 Jul 1994
Abstract :
This work presents nonlinear techniques applied to the speed control of a three-phase squirrel-cage induction motor. Artificial neural networks are used to develop adaptive control strategies based on nonlinear dynamic system identification and nonlinear parameter estimation. The principal results include a nonlinear rotor flux observer based on recurrent neural networks, and a nonlinear rotor time constant estimator based on nonrecurrent neural networks. Computer simulation results are obtained and the performance of the resulting adaptive field-oriented controller is analyzed
Keywords :
adaptive control; machine control; parameter estimation; recurrent neural nets; squirrel cage motors; velocity control; adaptive vector control; field-oriented controller; nonlinear dynamic system identification; nonlinear parameter estimation; nonlinear rotor flux observer; nonlinear rotor time constant estimator; nonlinear techniques; nonrecurrent neural networks; recurrent neural networks; speed control; three-phase squirrel-cage induction motor; Adaptive control; Artificial neural networks; Induction motors; Machine vector control; Nonlinear dynamical systems; Programmable control; Recurrent neural networks; Rotors; System identification; Velocity control;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374806