Title :
Maximum torque control of SynRM drive using ALM-FNN controller
Author :
Ko, Jae-Sub ; Choi, Jung-Sik ; Park, Ki-Tae ; Park, Byung-Sang ; Chung, Dong-Hwa
Author_Institution :
Sunchon Nat. Univ., Sunchon
Abstract :
The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter´s current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.
Keywords :
angular velocity control; electric current control; electric machine analysis computing; fuzzy neural nets; invertors; learning (artificial intelligence); machine control; neurocontrollers; reluctance motor drives; torque control; voltage control; adaptive learning mechanism-fuzzy neural network; artificial neural network; inverter current; maximum SynRM drive torque control; synchronous reluctance motor drive; voltage rated value; Artificial neural networks; Automatic control; Control engineering; Control systems; Fuzzy control; Instruments; Magnetic flux; Reluctance motors; Torque control; Voltage control; ALM-FNN; Artificial Neural Network; Maximum torque control; Synchronous Reluctance Motor;
Conference_Titel :
Control, Automation and Systems, 2007. ICCAS '07. International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-89-950038-6-2
Electronic_ISBN :
978-89-950038-6-2
DOI :
10.1109/ICCAS.2007.4406627