The Research Of Intelligent Controller Of BLDCM

Brushless DC motor has the features of time varying, nonlinearity and strong coupling. Although traditional control methods are of easy arithmetic, fast performance and precise control, it is difficult to meet the needs of static state and dynamic performance when the model of control subjects is uncertain. Intelligent control does not depend on exact math models and can restrain the impact of time-varying and parameter disturbance, so the intelligent control and the traditional control can complement each other.The intelligent control system of BLDCM is approached emphatically. Ripple torque' s rejection method, emulation programming are analysed in detail. Since BLDCM has rather ripple torque in operation with low speed, so a fuzzy neural network dual-mode control mode is applied in this paper through comparing fuzzy theory and neural network theory. Generation of BLDCM' s ripple torque is set forth detailedly, against generation corresponding method is put forward. How rejecting ripple torque which is generated from stator winding' s inversion is analysed in detail. Moreover the speed looping and current looping adopt respectively parameter self-adjustment fuzzy control and neural network control.Regarding real-time and the realization of micro-processor, the paper puts forward Kalman filter arithmetic and proves it. After the simulation of MATLAB, it is evident that the neural network adopting Kalman filter uses less learning time and restrains the noisy disturbance.After we analyzing the model of BLDCM, the intelligent controller is applied in the control system of BLDCM. The simulation results demonstrate that the intelligent control system of BLDCM has good static state and dynamic performance, and reduces torque tremble to some degree. In short, the control results can satisfy us.