Research Of BLDCM Control Methods With Particles Swarm Optimization

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 particles swarm optimization in the control of BLDCM system.Firstly, the thesis introduced BLDCM control system and the principle and the standard algorithm of PSO which is applied in this control system is introduced in detail. For the phenomenon that PSO may be failed into the local minimum while treating with high-dimensional's complied problem advanced an improved APSO. It pulled in the distributed entroy of particles and the advantaged distance of the particles in the evolution. These two performance indexes what is regulated the PSO's ability about the detection and the exploitation to detach the local minimum find the best global location. Used the standard test function and the neural network XOR tested algorithm proved that the improved APSO had better optimized efficiency and stronger stability.Secondly, Analyzed the mathematical model of BLDCM then the simulation model of BLDCM is built in MATLAB7.0.Thirdly, For making up the deficiency of traditional PID control, the PSO optimized the parameters of the PID is used. The strategy of searched particles with PSO is effective global optimization. Thence used the PSO optimized the parameters of the PID controlled the BLDCM's rotating speed what improved the dynamic response of the system, making studies of the simulation and the controller is to realize in the form of S function. The simulation shows the effictiveness of the proposed approach high speed responsibility and control precision, it has creatures of good adaptability and strong robustness.