Research On Brushless Direct Current Motor Drive System

The brushless direct-current motor(BLDCM) has many advantages that AC motor has, such as simple structure, reliable running, and maintanence convenience, and also has the characteristics that DC motor has, such as high operating efficiency, less excitation loss, and excellent drive performance. Therefore, it is developed and popularized very fleetly since it was created. At present, the BLDCM is used widely in various fields, such as number control machine tools, robotics, instruments, vehicles, computer periphery equipments, and so on. Now, the study of the BLDCM application is a very important area.First, this paper introduces the basic operational principle and the research actuality of the components of the BLDCM. Then, the simulation research process in MATLAB with Simulink and SimPowerSystems module based on the mathematical model, commutation principle and the PWM modulation modes are presented. Given the simulation model is novel to the actual drive system. This work provides the foundation to develop the drive system of the BLDCM based on DSP.The BLDCM drive system has been developed using the TI's TMS320F240 and MITSUBISHI'S PM30CSJ060, which can be running in real time. The performace of the drive system is good, and it provides the hardware condition to validate the result of the theory research.Two kinds of the network controls have been designed and implemented for this drive system. They are internet network control with PC server and the embedded network control with ARM server. The embedded network server is designed using ARM chip in order to adapt to the demand of the small and simple industrial equipments, such as small volume, low energy consumption, real time, and centralized systems.The ARM embedded network server is quite novel, the network control is reliable, stable, and convenience to operate, and the man-machine boundary is friendly.Application area of the BLDCM is enlarged continually, the drive system should have more excellent performance, but general PI control strategy usually couldn't satisfied. In the presence of various disturbances, an optimal state feedback control strategy with adaptive compensation is presented. The Kalman filter could restrain the random disturbance, the adaptive algorithm could compensate the reference input deviate, and the optimal state feedback control is capable of providing a very high-speed regulation and dynamic response over a wide range of operating conditions. The Simulated and experimental results indicate this control strategy not only could achieve the excellent drive performance, but also have the strong antidisturbance capability.As the nonlinearity and the uncertainty of the BLDCM mathematical model and wide range of operating conditions, the optimal state feedback control couldn't obtain the highlight effectiveness. So, this paper presents an improved fuzzy neural network control strategy to improve the control effect.The main advantages of this control strategy are as follows: the neural network identifier with RPE algorithm; the single neuron PID current controller; the fuzzy neural network speed controller with the different membership grade layer made of the little neural networks. The simulation result is satisfactory.This paper includes the novel software and hardware, and advanced control algorithms research.