Title :
DRNN network DTC in electromagnetic continuously variable transmission
Author :
Fu, Xingfeng ; Luo, Yutao ; Zhou, Sijia ; Zhang, Yinxian
Author_Institution :
Dept. of Automobile Eng., South China Univ. of Technol., Guangzhou
Abstract :
In order to reduce the serious fluctuation of torque, fluxes and stator current in electromagnetic continuously variable transmission motor direct torque control, an amendatory direct torque control method based on the diagonal recurrent neural network technology was presented in this paper. This method can reduce the torque and flux ripple in static run and enhance the performance of low speed. The simulation experimental results indicate that this method may be feasible alternative and high robust capabilities.
Keywords :
machine control; recurrent neural nets; torque control; DRNN network DTC; diagonal recurrent neural network technology; electromagnetic continuously variable transmission; flux ripple; motor direct torque control; Converters; Engines; Equations; Mechanical power transmission; Reluctance motors; Rotors; Shafts; Stator windings; Torque control; Vehicles; Direct Torque Control; Motor Timing System; Robust Characteristic;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4594151