Analyze and Comparison of Different PID Tuning Methods on a Brushless DC Motor Using Atmega328 Based Microcontroller Unit

Taşören, Ali Egemen; Orenbas, Halit; Şahin, Savaş

doi:10.1109/ceit.2018.8751768

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…PID "Proportional Integral Derivative" control method is one of the most used classical control techniques in industrial applications due to its simple and fast response time. PID control consists of the combination of proportional-integral and derivative control methods [11][12][13].…”

Section: Proportional Integral Derivative Control (Pid)mentioning

confidence: 99%

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID

GUNTAY,

SARITAS

2024

ijamec

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Brushless DC (BLDC) motors, which have small volumes, are widely used in many areas from the aviation industry to industrial applications due to their high efficiency and torque. In parallel with the development of technology, the field of use continues to expand with the development of BLDC engine (BLDCM) control strategies and the decrease in control costs. In this thesis study, it is aimed to minimize the observed changes in rotor speed compared to the reference speed. To achieve this, PID parameters were tried to be changed simultaneously with fuzzy control techniques, taking the error value as a reference. The control system of the BLDC engine was designed in the MATLAB/Simulink environment. In the simulation, the operating stability of classical PID and PID with updated fuzzy-based parameters on two engines with the same features was compared at different speeds. As a result of the research, it was concluded that the correction of the speed observed in the rotor of the PID-controlled motor, whose fuzzy logic-based coefficients were updated, based on the reference speed was more stable and the percentage of exceedance for the reference value was lower, compared to the classical PID controlled motor.

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Section: Proportional Integral Derivative Control (Pid)mentioning

confidence: 99%

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID

GUNTAY,

SARITAS

2024

ijamec

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“…Pada desain kontroler PID diperlukan tuning kontroler untuk menentukan parameter aksi kontrol Proportional, Integratif, dan Derifatif [6] [7]. Metode tuning bermacam-macam antara lain Ziegler Nichols (ZN), Algoritma Genetika atau Genetic Algoritm (GA), Fuzzy Gain Scheduling (FGS), Metode Tyreus -Luyben, Metode Damped Oscillation, Metode C-H-R, Metode Cohen-Coon, Metode Fertick, Metode Ciancone dan Marline, dan lain-lain [8] [12]. Jika dibandingkan dengan metode tuning lainnya, metode ZN dan GA dapat memberikan hasil yang baik pada pencapaian set point.…”

Section: Pendahuluanunclassified

Simulasi PMDC Menggunakan PID dengan Tuning Ziegler Nichols dan Algoritma Genetika

Ningtias,

Syafrudi

2022

elkom. jur. tekn. elekt. komp.

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Kecepatan motor DC Permanen Magnet (PMDC) dapat dikontrol menggunakan Proportional Integral Derivative (PID) kontroler. PID merupakan sebuah pengontrol yang paling banyak digunakan dalam bidang sistem kontrol di dunia industri yang dapat meningkatkan respon dinamik, mengurangi steady-state error, dan meningkatkan performa motor PMDC. Parameter aksi kontrol berupa KP, KI, dan KD dapat diperoleh melalui tuning. Tuning Ziegler Nichols dipilih karena rising time yang cepat dan steady-state error yang kecil. Sedangkan tuning Algoritma Genetika dipilih karena menciptakan KP, KI, dan KD terbaik secara otomatis. Metode penelitian yang digunakan adalah metode penelitian eksperimen dengan membandingkan performa antara tuning Ziegler Nichols dan Algoritma Genetika. Parameter performa motor PMDC berupa overshoot, rise time, settling time, dan steady-state error. Kontrol terbaik dari tuning Ziegler Nichols adalah PID dengan nilai KP = 1,2; KI = 6,108424536; dan KD = 0,058935 yang menghasilkan respon kecepatan dengan nilai overshoot 9,25%; rise time 0,04 detik; settling time 0,9 detik; dan steady-state error 0,008%. Sedangkan kontrol terbaik dari tuning Algoritma Genetika adalah PID dengan nilai KP = 0,426; KI = 4,468; dan KD = 0 yang menghasilkan respon kecepatan dengan nilai overshoot 0%; rise time 0,4 detik; settling time 0,4 detik; dan steady-state error 0,00016%. Sehingga kontrol terbaik adalah kontrol PID dengan metode tuning Algoritma Genetika.

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“…Conventional tuning strategies are based on the frequency response of the plant, but they are rarely used in real applications. For example, this is the case of the Ziegler-Nichols method, whose tuning rule can produce an oscillating response system if the plant is non-linear (Tasoren, Orenbas, and Sahin, 2018).…”

Section: Conclusionesmentioning

confidence: 99%

PID controller tuning using bacterial Quorum Sensing (QS)

Sarmiento¹,

Soto²

2020

Tecnura

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Objective: PID controllers are widely used to operate AC motors due to their simplicity and easy implementation. However, adjusting its parameters in search of an optimal scheme can be complex because it requires manual tuning by trial and error. This research aims to implement an optimized tuning scheme through a search based on the idealized behavior of a community of bacteria and its Quorum Sensing (QS). Methodology: A closed-loop system model with PID control considering disturbances is proposed in order to tune a disturbance-resistant controller. The response of the model is calculated using a search that mimics a simplified model of bacterial behavior. The scheme uses ITSE (Integral Time Squared Error) as the performance index. Results: The tuning resulting from the proposed scheme was evaluated by simulation and compared with tunings of the same model made by Root Locus and Genetic Algorithms (GA). The results showed a satisfactory response according the design criteria. Conclusions: Nowadays, PID controllers are still basic industrial control tools, particularly important in motor operation. The performance of these controls depends fundamentally on the design of their gain. In the case of complex plants, additional tools are required to facilitate PID tuning. We propose an intelligent and bio-inspired tuning scheme that demonstrates high performance in laboratory tests. Financing: University Francisco José de Caldas through the project 1-72-578-18.

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Analyze and Comparison of Different PID Tuning Methods on a Brushless DC Motor Using Atmega328 Based Microcontroller Unit

Cited by 11 publications

References 10 publications

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID

Simulasi PMDC Menggunakan PID dengan Tuning Ziegler Nichols dan Algoritma Genetika

PID controller tuning using bacterial Quorum Sensing (QS)

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