Title :
Study of Artificial Neural Network-Based Direct Torque Control System
Author :
Ma, Lixin ; Shi, Daonian ; Xing, Chengwu ; Liu, Heyong
Author_Institution :
Dept. of Electr. Eng., Univ. of Shanghai for Sci. & Technol., Shanghai, China
Abstract :
The direct torque control is introduced, and a controller for selecting the voltage space vector was designed with the supervised and fixed-weight neural network, the controller takes full advantage of the parallel computation, learning and fault-tolerant capability of artificial neural network (ANN) , so that it can cope with the time delay caused by the complex calculation required in traditional direct torque (DTC) and simplify the application of hardware. The simulation results show that the speed regulating system has good dynamic performance and the design is feasible.
Keywords :
angular velocity control; delays; induction motors; neurocontrollers; torque control; artificial neural network; direct torque control; time delay; voltage space vector selection; Artificial neural networks; Computer networks; Control systems; Delay effects; Hysteresis; Neural networks; Neurons; Space technology; Torque control; Voltage control;
Conference_Titel :
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4994-1
DOI :
10.1109/ICIECS.2009.5364173