Title :
Modeling of doubly salient permanent magnet motor based on ANFIS
Author :
Sun, Qiang ; Cheng, Ming
Author_Institution :
Dept. of Electr. & Electron. Eng., Hefei Univ., China
Abstract :
In this paper, the modeling based on adaptive-network-based fuzzy inference system (ANFIS) for doubly salient permanent magnet motor is developed for the first time. In the ANFIS, the hybrid learning algorithm combining the gradient method and the least square method is improved. The result of simulation shows that the modeling is of quick convergence and high accuracy. This model offers a possibility for the on-line real-time control of doubly salient permanent magnet motor.
Keywords :
adaptive systems; fuzzy reasoning; gradient methods; least squares approximations; permanent magnet motors; ANFIS; adaptive-network-based fuzzy inference system; doubly salient permanent magnet motor; gradient method; hybrid learning algorithm; least square method; online real-time control; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Neural networks; Permanent magnet motors; Power system modeling; Reluctance motors; Torque control;
Conference_Titel :
Electrical Machines and Systems, 2005. ICEMS 2005. Proceedings of the Eighth International Conference on
Print_ISBN :
7-5062-7407-8
DOI :
10.1109/ICEMS.2005.202538
