Title :
Tuning the stator resistance of induction motors using artificial neural network
Author :
Cabrera, L.A. ; Elbuluk, M.E.
Author_Institution :
Dept. of Electr. Eng., Akron Univ., OH, USA
Abstract :
Tuning the stator resistance of induction motors is very important, especially when it is used to implement direct torque control (DTC), which depends mainly on the stator resistance parameter. An artificial neural network is used in this paper to accomplish the tuning of the stator resistance of induction motors. The parallel recursive prediction error training algorithm was used to perform the training process of the neural network. The neural network executing the stator resistance tuning is trained alone online, making the conventional direct torque control strategy more robust and accurate. Finally, simulation results are presented for three different neural network configurations showing the efficacy of the tuning process.<>
Keywords :
control system analysis; control system synthesis; electric resistance; induction motors; learning (artificial intelligence); machine control; machine theory; neurocontrollers; robust control; stators; torque control; tuning; accuracy; artificial neural network; control system design; direct torque control; induction motors; parallel recursive prediction error training algorithm; robustness; simulation; stator resistance tuning; Artificial neural networks; Biological neural networks; Electric resistance; Electromagnetic measurements; Equations; Immune system; Induction motors; Robust control; Stators; Torque control;
Conference_Titel :
Power Electronics Specialists Conference, 1995. PESC '95 Record., 26th Annual IEEE
Conference_Location :
Atlanta, GA, USA
Print_ISBN :
0-7803-2730-6
DOI :
10.1109/PESC.1995.474845