Adaptive Speed Control for Brushless DC Motors Based On Genetic Algorithm and RBF Neural Network

Abstract

The brushless DC motors (BLDCM) are a multi-variable and non-linear system, so the research about the high performance of BLDCM is important, especially the control methods based on neural network. To solve the deficiency of neural network such as decision of structure and adjustment of parameters in hidden-unit, this paper presents an adaptive speed control approach based on genetic algorithm tuning radial basis function (RBF) neural network controller for brushless DC motor. In this approach, the RBF neural network whose structure and parameters of hidden-unit have been trained by genetic algorithm off-line constitutes a speed loop controller. The controller tunes parameters of neural network adaptively via the self-modifiability of network on-line, while the motor is running. At the same time, the current loop controller traces the change of given current rapidly, so that the system can adapt to variational environment. The results of experiments prove that the approach has lots of good performances in response speed, control accuracy, adaptability and robust.