Control For Brushless DC Motors Based On Genetic Algorithm And RBF Neural Network

The brushless DC motors (BLDCM) have small sizes, low weight, and large power to volume ratio, and receive much attention in every field. Compared with brush DC motors, BLDCM overcome a series drawbacks brought by mechanical converted direction. The BLDCM is a multi-variable and non-linear system, so the research about the high performance of speed regulation becomes an important direction. This paper researches the application of intelligence algorithm in the control of BLDCM system.Classical PID control is most common method in industry, but this method can't gain satisfying effect to the complex and non-linear object or process. Neural network control has the ability of expressing arbitrary nonlinear mapping, learning and self-adaptive. The selection of topology structure and initial value influence the capability of controller, and the method of trial and error has much difficult in getting optimization parameter controller. Genetic algorithm first put forwarded by Holland is a global optimization algorithm of random search that simulate the process of inheritance and evolution formed in natural condition. Parameter optimization based on genetic algorithm becomes an effective method in designing controller.To solve the deficiency of Radial Basis Function (RBF) 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. 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. At the same time, the current loop controller traces the change of given current rapidly, so that the system can adapt to the variational environment. The results of MATLAB simulation prove that the approach has lots of good performances in response speed, control accuracy, adaptability and robust. Finally, a hard circuit based on digital signal processor (DSP) TMS320F2812 is designed to further research speed regulation system of BLDCM.