Title :
Position Sensorless Control for Brushless DC Motor Based on RBFNN Optimized by Fast Recurvise Algorithm
Author :
Zhang Lin-sen ; Xie Shun-yi ; Yang Cheng-yu ; Yang Ying-hua
Author_Institution :
Dept. of Weaponry Eng., Naval Univ. of Eng., Wuhan, China
Abstract :
The principle of position sensorless control for brushless DC motors (BLDCM) is analyzed in this paper, and a new control method for BLDCM which is based on radial basis function(RBF) neural network optimized by fast recursive algorithm, is proposed due to perfect nonlinear mapping characteristic of neural network. Using FRA, the proposed method can determine the numbers and locations of the centers, and derive the weights between the hidden layer and the output layer. The effectively of this proposed position sensorless control method is verified by the simulation results.
Keywords :
brushless DC motors; machine control; neurocontrollers; nonlinear control systems; position control; radial basis function networks; recursive estimation; BLDCM; RBFNN optimisation; brushless DC motor; fast recurvise algorithm; hidden layer; nonlinear mapping characteristics; position sensorless control; radial basis function neural network; Brushless DC motors; Commutation; Couplings; DC motors; Feedforward neural networks; Neural networks; Optimization methods; Reluctance motors; Rotors; Sensorless control; RBF neural network; brushless DC motor; fast recurvise algorithm; sensorless;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.486
