Title :
On-line instant torque estimation of switched reluctance motor using adaptive B-spline neural network
Author :
Lin, Zhengyu ; Reay, Donald S. ; Williams, Barry W. ; He, Xiangning
Author_Institution :
Dept. of Electr., Electron. & Comput. Eng., Heriot-Watt Univ., Edinburgh, UK
Abstract :
A novel on-line instant torque estimation scheme for a switched reluctance motor (SRM) is presented. In the proposed method, an adaptive B-spline neural network is used to learn the non-linear flux-linkage and torque characteristics of a SRM. Due to the local nature of its generalisation properties, the training of the B-spline neural network is accomplished on-line and in real-time, and the system does not require a priori knowledge of the machine´s electromagnetic characteristics. The potential of the method is demonstrated successfully in simulation and experimentally using a 300 W 12/8 3-phase SRM.
Keywords :
estimation theory; learning (artificial intelligence); magnetic flux; neural nets; power engineering computing; reluctance motors; splines (mathematics); torque; 300 W; SRM; adaptive B-spline neural network; electromagnetic characteristics; nonlinear flux-linkage; online instant torque estimation; switched reluctance motor; torque characteristics; Adaptive systems; Computer networks; Couplings; Neural networks; Phase estimation; Physics computing; Reluctance machines; Reluctance motors; Spline; Torque measurement;
Conference_Titel :
Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE
Print_ISBN :
0-7803-7906-3
DOI :
10.1109/IECON.2003.1280187