# A Novel Design Methodology and Numerical Simulation of BLDC Motor for Power Loss Reduction
## 3 Simulation Results and Discussion  
The Landsman converter operating in the DICM functions as an internal PF preergulator to achieve a unity PF at the alternating current mains. PFR based on a Landsman converter is designed to operate in DICM for natural PF regulation at alternating current mains. The BLDC motors are modeled and tested with three types of controllers, and the characteristic curves are taken. The controllers are designed in such a way that they are incorporated for better control action and good performance. The various curves are taken for different controllers and analyzed. The optimum design of a BLDC motor controller is chosen. Figure 2 shows the simulation circuit of the PI controller. The Simulink model consists of a speed regulator, gates, decoder, inverter, and BLDC motor. The input of the PWM inverter is a DC supply. The inverter output is fed by the BLDCM. The location and velocity of the rotor are sensed by the tacho generator model and hall sensor. The reference speed is related to the output of the tacho generator. Figure 3 represents the simulation circuit of the PID controller. The PID controller is working alone under normal operating conditions. This control strategy requires an external part for the BLDC motor to change  
Figure 1: Landsman converter-based power loss reduction in BLDC motor. operating conditions. PID controllers are simple and can be used in a variety of controlled plants, but they are incapable of providing good control performance in nonlinear and higher-order systems. The responses of the BLDC motor are observed to be improved with PI and PID-based controllers. It is revealed from this investigation that the speed controllers used have their advantages and disadvantages, and the choice is made depending on the requirements of a particular application. It is also noticed that PI and PID controllers are easy to implement. Figure 4 shows the fuzzy controller simulation circuit. A fuzzy controller contains the following modules: rule-based, fuzzification module, database, and defuzzification module along with the database. The fuzzification module and the defuzzification module have two steps each. The methods involved in these modules have various types that are explained in the three divisions, namely: crisp variables, data flow, and computational flow. The fuzzy controller input is normalized by dividing the difference in speed by the reference speed, so that the fuzzy controller becomes universal. An adaptive fuzzy controller that changes the slope of the membership function according to error is also designed. Table 1 shows the % power loss measurement using CSC and Landsman converter under various stages. Table 2 shows the voltage and current stress for the two configurations. It concludes that Landsman configuration gives better results when compared to CSC configuration. We measured power factor values of 0.9764 and 0.9896 for 60 V and 200 V, respectively. The results show that the proposed approach works well and is a useful Power Factor Correction for BLDCM. The obtained result explains the difference in power loss in PFC, VSI, and BLDCM for both the CSC converter and the Landsman converter, respectively. The two different voltages are 200 V and 60 V for both the converters. This graph shows that the Landsman converter has less power loss than the CSC converter. The simulation results show the waveforms of the input voltage and the output from the LC filter with load variation. When the filter output is fed into the BL Landsman converter, an output current and voltage are obtained. The simulation output of rotor speed for the PI controller is shown in Figure 5. Simulation results of BLDCM speed response through varying designs of fuzzy, fuzzy tuned PI, PID controller, and fuzzy tuned supervised offline fuzzy controllers, keeping constant load torque of 25 Nm with a reference speed of 1500 rpm.