Abstract
Brushless direct current (BLDC) Motors are extensively used because of their characteristics. Such characteristics are high dynamic response and high-power density. BLDCM control system is a nonlinear, multi-variable, strong-coupling system. In this paper it is proposed that a neural network controller is used for the five level switch of the BLDC motor to enhance the power factor and reduce the current distortions with respect to its rise time, startup torque. This method is also done in comparison with the PID controllers. The working principle of the BLDC is with the help of five-switch control scheme can be implemented here. The different values of load were used to consider the total operation of the BLDC motor is to be controlled. After the completion of the training and testing of the neural network, it might be maintain the constant load values and its variables. To calculate the duty ratio of the DC-DC converter, it will be adjusted to regulate the speed of the BLDC motor. However the DC link of the five switching inverter is used here for the boosting of the voltage. The effectiveness of the proposed control technique can be realized with help of the speed sensor. Various tests have been conducted in the simulation the proposed technology is the robust technology and it is proven that very effective and suitable control technique.
Highlights
Brushless DC motor (BLDCM) is a very broadly used one in the applications of the Electrical Engineering, Automobile and mining, Industrial applications due its feasibility and advantages of high performance, rugged maintainance, smooth operation and etc., Brushless DC motor (BLDCM) has been widely used in electrical equipment, mining and other industrial application fields due to its advantages of high power density, high efficiency, easy maintenance, silent operation and so on[1]-[3]
To finding the problems like speed response, steady state error, power factor improvement in Brushless DC motor, and the conventional controllers are not sufficient controllers. Instead of using these conventional controllers it is suggested that the non conventional controllers like fuzzy logic control technique, neural network technique will be the effective controllers for the Brushless DC motor
The test results are carried out in this paper, it was seen that the control implemented from the proposed neural network scheme is wellorganized, and reasonable results than the conventional controllers in the presence of constant load are obtained
Summary
Brushless DC motor (BLDCM) is a very broadly used one in the applications of the Electrical Engineering, Automobile and mining, Industrial applications due its feasibility and advantages of high performance, rugged maintainance, smooth operation and etc., Brushless DC motor (BLDCM) has been widely used in electrical equipment, mining and other industrial application fields due to its advantages of high power density, high efficiency, easy maintenance, silent operation and so on[1]-[3]. We show the three-phase inverter, which has a sensor to monitor the current supplied to the BLDC. It is possible to see that the BLDC motor is coupled with a sensor for speed reading This sensor generates the control signal that determines the pulse width of the PWM (Pulse Width Modulation) that activates each of the inverter’s drivers, in addition to the necessary switching stage that depends on the state of the Hall Effect sensors. This switching allows the three-phase inverter to function normally in addition to preventing a short circuit in any of the three branches.
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