Title :
Switched Reluctance Motors Direct Torque Control Research Based on RBF Neural Network and Fuzzy Adaptive PID Controller
Author :
Hang, Jun ; Huang, You-Rui ; Li, Li
Author_Institution :
Inst. of Electr. & Inf. Eng., Anhui Univ. of Sci. & Technol., Huainan, China
Abstract :
For the defects of conventional direct torque control (DTC) system of switched reluctance motors (SRM), RBF neural network (RBFNN) and fuzzy adaptive PID controller are applied to direct torque control system of SRM in the paper. Switching state table is replaced by RBFNN; fuzzy adaptive PID controller is applied to the outer loop for speed adjustment. In order to verify the validity of the method, simulation is carried out based on the Matlab7.1. Results of experiment show that the control system has good performances.
Keywords :
adaptive control; fuzzy control; neurocontrollers; radial basis function networks; reluctance motor drives; three-term control; torque control; Matlab; RBF neural network; direct torque control; fuzzy adaptive PID controller; switched reluctance motor; switching state table; Adaptive systems; Artificial neural networks; Control systems; Niobium; Reluctance motors; Torque; DTC; Fuzzy Adaptive PID; RBFNN; SRM; Simulation;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2010 2nd International Conference on
Conference_Location :
Nanjing, Jiangsu
Print_ISBN :
978-1-4244-7869-9
DOI :
10.1109/IHMSC.2010.41