Simulation of Brushless DC Motor Speed Control with Fuzzy Logic Method

Abstract

Brushless DC Motor is used frequently in industrial applications, because this type of motor has high efficiency and does not cause sparks. However, the speed control of this motor generally has a slow response to the set point and the speed is not constant when given different loads. Therefore, it is essential to design sophisticated speed control system for this motor to improve the constant speed and speed response. In this paper, fuzzy logic method for speed control system is used to give the decision of how much voltage will be added to the motor to get to the desired speed set point value, and how much voltage will be reduced if the speed of the motor passes through the speed set point. brushless dc motor speed is set with input voltage to the motor taking into account speed error and delta speed error using Fuzzy Logic, so that when the speed of the motor almost reaches the set point the input voltage to the motor is reduced so as not to overshoot until the speed of the motor reaches the set point. The results of this study gave a better response than not using fuzzy logic methods. Where at set point 270 gets response time to set point (Tr) faster 0.0002 seconds from 0.0046 seconds to 0.0044, the time to steady state (Ts) is faster 0.025 seconds from 0.04 seconds to 0.015 seconds, the maximum pass percentage (Mp) is reduced from 75% to 20%, and error speed from 1.5% to 0%. And for a trial load at t=0.025 seconds in set point 270 with a load of 4Nm responds to the set point back for 0.014 seconds.

Application Of Sugeno's Fuzzy Inference System In Determining Inventory Goat Milk

“…In fuzzy logic, the membership value has the main characteristic that it lies in the range of values between 0 to 1 [1], [2], [3]. Fuzzy logic can be an alternative in solving a problem because Fuzzy Logic has several advantages such as having easy-to-understand concepts, being able to adapt to uncertainty or change, being able to describe linear functions as a whole, or having tolerance for inaccurate data [4], [5]. In fuzzy logic there are several algorithms that can be used, namely Fuzzy Tsukamoto, Fuzzy Sugeno, and Fuzzy Mamdani.…”