DocumentCode :
2599281
Title :
An ANN optimal preview controller technique for induction motor
Author :
Negm, M.M.M. ; Mantawy, A.H.
Author_Institution :
Dept. of Electr. Eng., Ain-Shams Univ., Cairo, Egypt
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
966
Abstract :
An artificial neural networks (ANN) technique for on-line speed control of a three-phase induction motor (IM) is presented in this paper. This novel technique is based on the optimal preview controller. The proposed technique comprises a new error system and vector control of the IM. Preview feedforward steps are introduced into the control law to enhance the transient response and to improve the robustness of the controlled system. A feedforward neural network trained with the backpropagation algorithm has been developed to embody the characteristic of the above optimal preview controller within a small, accurate and global system. The training was successful over a large range of training data. Test results conducted over several data ranges have shown accurate and fast performance in predicting the controller output variables
Keywords :
angular velocity control; backpropagation; feedforward neural nets; induction motors; machine vector control; neurocontrollers; optimal control; predictive control; transient response; ANN optimal preview controller; artificial neural networks; backpropagation algorithm; error system; feedforward; feedforward neural network; induction motor; on-line speed control; robustness improvement; three-phase induction motor; transient response enhancement; vector control; Artificial neural networks; Control systems; Error correction; Induction motors; Machine vector control; Neural networks; Optimal control; Robust control; Transient response; Velocity control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industry Applications Conference, 2000. Conference Record of the 2000 IEEE
Conference_Location :
Rome
ISSN :
0197-2618
Print_ISBN :
0-7803-6401-5
Type :
conf
DOI :
10.1109/IAS.2000.881949
Filename :
881949
Link To Document :
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