The Speed Control Of Brushless DC Motor Based On Genetic Fuzzy Algorithm

The brushless DC motor (BLDCM) has a series of advantages, such as simple structure, high reliability, convenient maintenance, high efficiency, no excitation loss and so on. So far, BLDCM has been widely used in many fields. Therefore, well-designed high-performance BLDCM controller has important practical significance and practical value. This thesis studies the speed control of BLDCM. The BLDCM is a multi-variable, strong coupling and nonlinear system, the traditional controllers have failed to perform satisfactorily, this thesis presents a fuzzy control based on Genetic Algorithm.This thesis analyses the basic structure, working principle, mathematical models and transfer functions of the BLDCM. A dual-loop control system is designed, the outer is speed loop and the inner is current loop. The current loop uses the conventional PI controller and the speed loop uses genetic fuzzy algorithm intelligent controller, the membership functions, control rule sand factors are optimized by genetic algorithm. The simulation results of MATLAB show that fuzzy control based on genetic algorithm has no overshoot, response faster and has better stability.In this thesis, the control system is EL-SMCK which is made by Dasheng Technology Company of Beijing. In order to achieve high-precision control of the BLDCM, this thesis chooses TMS320F28335which is made by TI as the motor control chip. The hardware and software of the control system are researched. Finally, this thesis analyzes the experimental results.