PENGONTROL LOGIKA FUZZY UNTUK MENGENDALIKAN KECEPATAN ROTOR MOTOR ARUS SEARAH

Fuzzy Logic Controller (FLC) telah banyak digunakan dalam aplikasi untuk mengontrol kecepatan motor arus searah. Kinerja dengan kemampuan untuk mengontrol secara otomatis diperlukan. Dalam proyek ini, kinerja motor arus searah yang dipilih telah dikontrol menggunakan pengontrol Proportional Integral Derivative (PID) dan FLC telah dipelajari. Untuk tujuan itu, nilai maksimum kecepatan swap untuk mencapai nilai kecepatan yang telah ditentukan dan juga waktu yang dibutuhkan untuk mencapai nilai kecepatan target yang stabil telah diidentifikasi dan dianalisis untuk memverifikasi perilaku pengontrol PID serta FLC. Oleh karena itu, penyesuaian harus dilakukan pada FLC untuk memastikan bahwa kinerja yang dihasilkan sesuai dengan yang diinginkan... Kontrol kecepatan motor arus searah yang dilakukan oleh pengontrol PID dan pengontrol FLC adalah dengan menggunakan perangkat lunak MATLAB. Hasil menunjukkan bahwa menggunakan FLC, kecepatan maksimum dari kemungkinan adalah minimal, gelombang transien minimum dan juga nilai kesalahan dalam kondisi kecepatan stabil minimum telah dicapai dibandingkan dengan menggunakan pengontrol Controlled Integral Derivative (PID) konvensional.

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

Journal of Vibration and Control ◽

10.1177/1077546319890188 ◽

2019 ◽

Vol 26 (13-14) ◽

pp. 1187-1198 ◽

Author(s):

Li-Xin Guo ◽

Dinh-Nam Dao

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Deep Learning ◽

Fuzzy Logic Controller ◽

Control Method ◽

Time Delay Estimation ◽

Delay Estimation ◽

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

Design of microcontroller-based decentralized controller board to drive chiller systems using PID and fuzzy logic algorithms

10.1177/0954408919885678 ◽

2019 ◽

Vol 234 (1) ◽

pp. 98-107 ◽

Author(s):

Yalcin Isler ◽

Savas Sahin ◽

Orhan Ekren ◽

Cuneyt Guzelis

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Low Cost ◽

Variable Speed ◽

Scroll Compressor ◽

Proportional Integral ◽

Output Controller ◽

Expansion Valve ◽

Controller Board

This study deals with designing a decentralized multi-input multi-output controller board based on a low-cost microcontroller, which drives both parts of variable-speed scroll compressor and electronic-type expansion valve simultaneously in a chiller system. This study aims to show the applicability of commercial low-cost microcontroller to increase the efficiency of the chiller system, having variable-speed scroll compressor and electronic-type expansion valve with a new electronic card. Moreover, the refrigerant system proposed in this study provides the compactness, mobility, and flexibility, and also a decrease in the controller unit’s budget. The study was tested on a chiller system that consists of an air-cooled condenser, a variable-speed scroll compressor, and a stepper driven electronic-type expansion valve. The R134a was used as a refrigerant fluid and its flow was controlled by electronic-type expansion valve in this setup. Both variable-speed scroll compressor and electronic-type expansion valve were driven by the proposed hardware using either proportional integral derivative or fuzzy logic controller, which defines four distinct controller modes. The experimental results show that fuzzy logic controlled electronic-type expansion valve and proportional integral derivative controlled variable-speed scroll compressor mode give more robustness by considering the response time.

Comparative study of Fuzzy Logic controller and Proportional Integral Derivative controller on DC-DC Buck Converter

2010 4th International Power Engineering and Optimization Conference (PEOCO) ◽

10.1109/peoco.2010.5559170 ◽

2010 ◽

Cited By ~ 6

Author(s):

M.J Yusoff ◽

N.F Nik Ismail ◽

Ismail Musirin ◽

N. Hashim ◽

D. Johari

Keyword(s):

Fuzzy Logic ◽

Comparative Study ◽

Fuzzy Logic Controller ◽

Buck Converter ◽

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014] ◽

Comparative analysis of Proportional Integral Derivative controller and Fuzzy Logic controller on Interleaved Soft Switching Boost converter

10.1109/iccpct.2014.7054893 ◽

2014 ◽

Author(s):

M. Deepika ◽

M. Marimuthu ◽

B. Paranthagan

Keyword(s):

Fuzzy Logic ◽

Comparative Analysis ◽

Fuzzy Logic Controller ◽

Boost Converter ◽

Soft Switching ◽

Comparison between proportional, integral, derivative controller and fuzzy logic approaches on controlling quarter car suspension system

MATEC Web of Conferences ◽

10.1051/matecconf/201818402018 ◽

2018 ◽

Vol 184 ◽

pp. 02018

Author(s):

Ahmet Mehmet Karadeniz ◽

Alsabbagh Ammar ◽

Husi Dr.Geza

Keyword(s):

Fuzzy Logic ◽

Public Transportation ◽

Fuzzy Logic Controller ◽

Suspension System ◽

Maximum Efficiency ◽

Proportional Integral ◽

Car Suspension ◽

Quarter Car

Developing and constantly changing technologies, efforts to achieve maximum efficiency with minimum fuel consumption, as well as the development of comfort and safety systems, have become very essential topic in car manufacturing and design. Whereas comfort and security were not given a high importance in the first produced cars, they are indispensable elements of today's automobiles. Since public transportation uses road in large scale, the need for safety and repose is also increasing. Nowadays, vehicles have better security and comfort systems, which react very quickly to all kinds of loads and different cases of driving (braking, acceleration, high speed, cornering), where the tires can keep the road at its best, utilizing an advanced suspension system. In this study, a quarter-car model was fulfilled using LabVIEW (Laboratory Virtual Instrumentation Engineering Workbench) software. The control of this model has been realized by applying two different controllers. PID (proportional, integral, derivative) controller which is a common and conventional control method and the Fuzzy Logic controller which is considered as an expert system that is becoming more and more widely used. In both control approaches, controlling the suspension system was achieved successfully. However; It has been determined that controlling the system using Fuzzy Logic controller gave better dynamic response than applying the PID controller for the quarter car suspension model that has been used in the direction of this study.

Comparison between Fuzzy Logic Controller (FLC) and Fractional Order Proportional Integral Derivative (FOPID) Controller on Water Level and Steam Temperature of Steam Drum Boiler

International Journal of Computing Science and Applied Mathematics ◽

10.12962/j24775401.v4i2.3492 ◽

2018 ◽

Vol 4 (2) ◽

pp. 56

Author(s):

Zainullah Zuhri ◽

Mardlijah Mardlijah ◽

Didik Khusnul Arif

Keyword(s):

Fuzzy Logic ◽

Water Level ◽

Fractional Order ◽

Fuzzy Logic Controller ◽

Steam Temperature ◽

Drum Boiler ◽

Proportional Integral ◽

Steam Drum

A comparative study of Proportional Integral Derivative controller and Fuzzy Logic controller on DC/DC Buck-Boost Converter

2011 IEEE Symposium on Industrial Electronics and Applications ◽

10.1109/isiea.2011.6108687 ◽

2011 ◽

Author(s):

Nik Fasdi Nik Ismail ◽

Norazlan Hashim ◽

Rahimi Baharom

Keyword(s):

Fuzzy Logic ◽

Comparative Study ◽

Fuzzy Logic Controller ◽

Boost Converter ◽

Development of Proportional–Integral–Derivative and Fuzzy Control Strategies for Navigation in Agricultural Environments

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4038504 ◽

2017 ◽

Vol 140 (6) ◽

Author(s):

Stephanie Bonadies ◽

Neal Smith ◽

Nathan Niewoehner ◽

Andrew S. Lee ◽

Alan M. Lefcourt ◽

...

Keyword(s):

Fuzzy Logic ◽

Food Quality ◽

Pid Controller ◽

Fuzzy Logic Controller ◽

Control Strategies ◽

Working Hours ◽

Quality And Safety ◽

Proportional Integral ◽

Food Quality And Safety

Farming and agriculture is an area that may benefit from improved use of automation in order to increase working hours and improve food quality and safety. In this paper, a commercial robot was purchased and modified, and crop row navigational software was developed to allow the ground-based robot to autonomously navigate a crop row setting. A proportional–integral–derivative (PID) controller and a fuzzy logic controller were developed to compare the efficacy of each controller based on which controller navigated the crop row more reliably. Results of the testing indicate that both controllers perform well, with some differences depending on the scenario.

Design and simulation of a normalized fuzzy logic controller for the quadruple-tank process

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i1.pp227-234 ◽

2020 ◽

Vol 18 (1) ◽

pp. 227

Author(s):

Wameedh Riyadh Abdul-Adheem

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Industrial Processes ◽

Stable Operation ◽

Design Tools ◽

Proportional Integral ◽

Control Techniques ◽

Classical Control

Industrial processes include multivariable systems and nonlinearities. These conditions must be effectively controlled to ensure a stable operation. A proportional–integral–derivative controller and other classical control techniques provide simple design tools to designers, but cannot accommodate nonlinearities in industrial processes. In this study, the quadruple-tank process, which is one of the most widely used processes in the chemical industry, was selected as the research object. To examine this process, a fuzzy logic controller, instead of an exact mathematical model, was proposed to ensure the reliability of the experience. A modification was proposed to facilitate the design process. To check the validity of the proposed controller, it was compared with the conventional proportional–integral controller. The former exhibited acceptable performance.<table class="MsoTableGrid" style="width: 444.85pt; border-collapse: collapse; border: none; mso-border-alt: solid windowtext .5pt; mso-yfti-tbllook: 1184; mso-padding-alt: 0cm 5.4pt 0cm 5.4pt;" width="0" border="1" cellspacing="0" cellpadding="0"><tbody><tr style="mso-yfti-irow: 0; mso-yfti-firstrow: yes; mso-yfti-lastrow: yes; height: 63.4pt;"><td style="width: 290.6pt; border: none; border-top: solid windowtext 1.0pt; mso-border-top-alt: solid windowtext .5pt; padding: 0cm 5.4pt 0cm 5.4pt; height: 63.4pt;" valign="top" width="593"><span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">Industrial processes include multivariable systems and nonlinearities. These conditions must be effectively controlled to ensure a stable operation. A proportional–integral–derivative controller and other classical control techniques provide simple design tools to designers, but cannot accommodate nonlinearities in industrial processes. In this study, the quadruple<span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;">-tank process<span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">, which is one of the most widely used processes in the chemical industry, was selected as the research object. To examine this process, a<span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;"> fuzzy logic controller, instead of an <span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">exact mathematical model,<span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">was proposed to ensure the reliability of the experience. <span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;">A modification was proposed to facilitate the design process. To check the validity of the proposed controller, it was compared with the conventional proportional<span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi;">–<span style="mso-ascii-font-family: 'Times New Roman'; mso-ascii-theme-font: major-bidi; mso-hansi-font-family: 'Times New Roman'; mso-hansi-theme-font: major-bidi; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: major-bidi; mso-bidi-language: AR-IQ;">integral controller. The former exhibited acceptable performance. </td></tr></tbody></table>

Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller

Advances in Mechanical Engineering ◽

10.1177/1687814019890199 ◽

2019 ◽

Vol 11 (11) ◽

pp. 168781401989019 ◽

Cited By ~ 6

Author(s):

Huangshui Hu ◽

Tingting Wang ◽

Siyuan Zhao ◽

Chuhang Wang

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Direct Current ◽

Fuzzy Logic Controller ◽

Speed Control ◽

Operating Conditions ◽

Superior Performance ◽

Membership Functions ◽

Proportional Integral ◽

Differential Type

In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.