Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network

A. Obed, Adel; L. Saleh, Ameer

doi:10.33762/eeej.2014.95599

	Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network
Iraqi Journal for Electrical And Electronic Engineering
Article 1, Volume 10, Issue 2, 2014, Pages 118-129 PDF (0 K)
DOI: 10.33762/eeej.2014.95599
Authors
Adel A. Obed; Ameer L. Saleh
Abstract
In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.
Keywords
BLDC motor; modeling and simulation; Recurrent WNN; PSO algorithm

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