Controlling Speed Of Brushless DC Motor By Using Fuzzy Logic Controller

Auliansyah, Fahrizal; Sutedjo,; Qudsi, Ony Asrarul; Ferdiansyah, Indra

doi:10.1109/isemantic50169.2020.9234290

Cited by 9 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The BLDC motor drive is successfully controlled in detail in this article using a real-time [8]. In 2020 Auliansyah [9] described the use of a fuzzy logic controller to regulate the speed of a BLDC motor. The output of a 3-phase inverter is controlled in this study using the PWM approach.…”

Section: Introductionmentioning

confidence: 99%

“…The developed fuzzy logic controller has two inputs and one output parameter. By using the designed fuzzy logic controller, the BLDC motor's speed response may be made to spin in accordance with the specified set point value [9]. In 2021 Agrawal and other used a speed-based neural controller to develop on a soft tuning-based controller that may provide noticeably superior outcomes for that.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers

Shary

Nekad

Alawan

2023

IJEECS

View full text Add to dashboard Cite

One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best response for BLDC motor speed. The PID controller in the third method based on neural network also give best reaction on motor speed. Finally, comparison made in speed and torque profiles by using sudden changes in speed and load torque under the three proposed methods. The results show when using first controller the speed rise to 1,526 r.p.m and drop to 1,400 r.p.m at the test conditions. These oscillations will disappear when using the second and third controller.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers

Shary

Nekad

Alawan

2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…The related work to control the DC motor has been done before. Some of them is Linear Quadratic Regulator [39], Fractional Order PID [40], PID Control [41][42], PID Control [43], Fuzzy Logic Controller [44], State Observer [45]. However, the research only uses simulation, not hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

Stirring System Design for Automatic Coffee Maker Using OMRON PLC and PID Control

Setiawan

Ma’arif

2021

IJRCS

View full text Add to dashboard Cite

The implementation of the use of PLC in this study was designed for a small scale in the process of stirring coffee drinks at a speed of 600 RPM. To get a stable speed so that the water does not overflow, a control system is needed. To get optimal results, a system design that can control the stirring speed is arranged automatically using a Programmable Logic Controller (PLC). This system is designed using a rotary encoder sensor C38S6G5-600B-G24N as a speed reader obtained from the movement of the motor, DC Motor JGA25-370 12V as an actuator or stirrer. PLC OMRON CP1E-NA20DR-A is used as a motor speed control device using the Proportional Integral Derivative (PID) algorithm to control the system according to the setpoint entered. The motor speed control system with the PID algorithm shows a system response that works well according to the researchers' expectations. The response of the system obtained is fast enough to achieve a stable speed with a small overshoot value. Thus this system was successfully designed to control the stirring process of coffee drinks automatically and produce stable stirring by giving a set point of 600 RPM at the parameter constant Proportional band is 720%; Integral time is 1.6s; and Derivative time is 0.2s with a rise time value is 1.3s; settling time is 11s; overshoot is 1.1%; and steady-state error is 0.5%.

show abstract