Research On BLDCM Speed Regulation System Based On Intelligent Control Algorithm

Because of its simple structure,high efficiency,convenient control,reliable operation,brushless DC motor(BLDCM)has been widely used in the field of automobile driving,civil aviation,industry,military and so on.Furthermore,with the development of electronics,computer and automation technology,people put forward higher requirements on the control performance of the brushless DC motor.Therefore,It has very broad application prospects that research on brushless DC motor control system of faster response speed,higher control precision and more stable and reliable performance.It is difficult to satisfy the high speed,high precision or large load disturbance occasions with traditional speed control system of brushless DC motor.So,this paper studied the BLDCM speed control system which can be applied to the automatic temperature measurement robot of industrial coke oven based on the intelligent control algorithm.According to its movement requirements of more accurate,faster,stronger anti-interference ability and avoid sliding,the fuzzy PID speed control system based on improved particle swarm optimization algorithm was used to control brushless DC motor of the temperature measuring robot.Firstly,it analyzed the composition and working principle of the brushless DC motor,established the mathematical model under ideal conditions,got the transfer function after Laplasse transform,and built a simulation model in the Simulink.Then,it analyzed the basic principle of fuzzy control algorithm,established the fuzzy control rules,and applied the fuzzy control algorithm into PID control.This algorithm not only retained the robustness of PID control,but also reflected the dynamic control quality of fuzzy control.It made the original control system had good adaptability,and fully showed the superiority of the fuzzy PID control algorithm.Finally,it introduced the particle swarm optimization algorithm principle,execution flow and its mathematical expression,and used the orthogonal test and mutation strategy mechanism of imitating chromosome variation mechanism to improving the traditional particle swarm optimization algorithm which had the defect of slow iteration speed and easily falling into local optimum.This paper illustrated the improvement effect of orthogonal test on the speed and efficiency of the algorithm by giving an example.And in order to keeping the diversity of the algorithm in the process of iteration,the mutation strategy of imitating gene mutation mechanism was introduced to improving the optimal value transfer process of the algorithm.It can avoid the phenomenon of premature aggregating and falling into local optimum.According to the introduction of these two strategies,the pseudo code of the improved algorithm was given.The effectiveness of the proposed method is verified by MATLAB programming and benchmark function contrast experiments.The improved particle swarm optimization algorithm applying in the optimization of fuzzy PID control algorithm,obviously improved the control quality of the regulator and made the brushless DC motor speed control system had good accuracy,responsive speed,stability and adaptability.