Speed Control of BLDC Motor with Ripple Effect Reduction Using Recurrent Wavelet Neural Networks

Abstract

The interest in research and use of the Brushless DC (BLDC) motor has increased significantly over the last few years, due to development of electric vehicles (EV) as well as to its use in other growing areas. With further predicted growth of the EV industry, it is expected that the BLDC motor will become even more significant. BLDC motor is a highly nonlinear, dynamic system, which in terms of its control makes tuning of controllers difficult, especially when high precision is required. Because of its operating principle BLDC motor also has issues with torque ripple which affects the speed response in a speed controlled system. In this paper an intelligent controller that combines a proportional-integral-derivative (PID) controller with a recurrent wavelet neural network is proposed. By using an intelligent controller, as an adition to a standard PID, the approximation ability of neural networks is used not only to deal with the nonlinearity of the system, but also to reduce the torque ripple influence on the speed response. By computer simulations it is verified that the proposed controller is able to completely neutralize the ripple effect and achieve good transient speed response. It is also shown that improved robustness against load disturbance compared to the system containing only a PID controller is achieved.