The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. 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 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. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive.
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