Sand cat swarm optimization for controlling PID in DC motor

In this article, the direct current (DC) motor control approach is presented using the sand cat swarm optimization (SCSO) method to obtain the best proportional integral derivative (PID) parameters. DC motors are popular equipment. In addition, DC motors are easy to apply. SCSO is a method that adop...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Telkomnika 2024-04, Vol.22 (2), p.462-470
Hauptverfasser: Aribowo, Widi, Ghith, Ehab Saif, Rahmadian, Reza, Widyartono, Mahendra, Lukita Wardani, Ayusta, Prapanca, Aditya, Laksmi B., Nur Vidia
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 470
container_issue 2
container_start_page 462
container_title Telkomnika
container_volume 22
creator Aribowo, Widi
Ghith, Ehab Saif
Rahmadian, Reza
Widyartono, Mahendra
Lukita Wardani, Ayusta
Prapanca, Aditya
Laksmi B., Nur Vidia
description In this article, the direct current (DC) motor control approach is presented using the sand cat swarm optimization (SCSO) method to obtain the best proportional integral derivative (PID) parameters. DC motors are popular equipment. In addition, DC motors are easy to apply. SCSO is a method that adopts the desert cat's life in nature when searching for prey. This cat is able to detect low frequencies below 2 kHz and also has an extraordinary ability to dig for prey. This research was carried out using the MATLAB/Simulink application. To obtain the performance of the SCSO method, a comparison method was used, namely particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and aquila optimizer (AO). From the results of the study, it was found that the settling time value and integral total weighted absolute value error (ITAE) value of the SCSO method were the best compared to other methods.
doi_str_mv 10.12928/telkomnika.v22i2.25630
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3048504582</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3048504582</sourcerecordid><originalsourceid>FETCH-LOGICAL-c146t-73c7b7b17746b4a6633190b81afd010ab32edd4e8ee19d593661c65fb43eff223</originalsourceid><addsrcrecordid>eNpFkF9LwzAAxIMoOOY-gwGfO_OvSYNPsukcDBTU55C2iWRrk5pkin566yZ4cNzLcQc_AC4xmmMiSXWdTbcLvXc7Pf8gxJE5KTlFJ2BCKCKFJJKeggnmkhaj0TmYpbRFowQipawm4OZZ-xY2OsP0qWMPw5Bd7751dsFDGyJsgs8xdJ3zb_BpvYTOw-UC9iGHeAHOrO6Smf3lFLze370sHorN42q9uN0UDWY8F4I2ohY1FoLxmmnOKcUS1RXWtkUY6ZoS07bMVMZg2ZaSco4bXtqaUWMtIXQKro67Qwzve5Oy2oZ99OOloohVJWJl9dsSx1YTQ0rRWDVE1-v4pTBSB1jqH5Y6wFIHWPQHH-Ff4g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3048504582</pqid></control><display><type>article</type><title>Sand cat swarm optimization for controlling PID in DC motor</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Aribowo, Widi ; Ghith, Ehab Saif ; Rahmadian, Reza ; Widyartono, Mahendra ; Lukita Wardani, Ayusta ; Prapanca, Aditya ; Laksmi B., Nur Vidia</creator><creatorcontrib>Aribowo, Widi ; Ghith, Ehab Saif ; Rahmadian, Reza ; Widyartono, Mahendra ; Lukita Wardani, Ayusta ; Prapanca, Aditya ; Laksmi B., Nur Vidia</creatorcontrib><description>In this article, the direct current (DC) motor control approach is presented using the sand cat swarm optimization (SCSO) method to obtain the best proportional integral derivative (PID) parameters. DC motors are popular equipment. In addition, DC motors are easy to apply. SCSO is a method that adopts the desert cat's life in nature when searching for prey. This cat is able to detect low frequencies below 2 kHz and also has an extraordinary ability to dig for prey. This research was carried out using the MATLAB/Simulink application. To obtain the performance of the SCSO method, a comparison method was used, namely particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and aquila optimizer (AO). From the results of the study, it was found that the settling time value and integral total weighted absolute value error (ITAE) value of the SCSO method were the best compared to other methods.</description><identifier>ISSN: 1693-6930</identifier><identifier>EISSN: 2302-9293</identifier><identifier>DOI: 10.12928/telkomnika.v22i2.25630</identifier><language>eng</language><publisher>Yogyakarta: Ahmad Dahlan University</publisher><subject>Algorithms ; Cats ; D C motors ; Electric motors ; Exploitation ; Methods ; Optimization algorithms ; Particle swarm optimization ; Phase transitions ; Proportional integral derivative ; Wolves</subject><ispartof>Telkomnika, 2024-04, Vol.22 (2), p.462-470</ispartof><rights>2024. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Aribowo, Widi</creatorcontrib><creatorcontrib>Ghith, Ehab Saif</creatorcontrib><creatorcontrib>Rahmadian, Reza</creatorcontrib><creatorcontrib>Widyartono, Mahendra</creatorcontrib><creatorcontrib>Lukita Wardani, Ayusta</creatorcontrib><creatorcontrib>Prapanca, Aditya</creatorcontrib><creatorcontrib>Laksmi B., Nur Vidia</creatorcontrib><title>Sand cat swarm optimization for controlling PID in DC motor</title><title>Telkomnika</title><description>In this article, the direct current (DC) motor control approach is presented using the sand cat swarm optimization (SCSO) method to obtain the best proportional integral derivative (PID) parameters. DC motors are popular equipment. In addition, DC motors are easy to apply. SCSO is a method that adopts the desert cat's life in nature when searching for prey. This cat is able to detect low frequencies below 2 kHz and also has an extraordinary ability to dig for prey. This research was carried out using the MATLAB/Simulink application. To obtain the performance of the SCSO method, a comparison method was used, namely particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and aquila optimizer (AO). From the results of the study, it was found that the settling time value and integral total weighted absolute value error (ITAE) value of the SCSO method were the best compared to other methods.</description><subject>Algorithms</subject><subject>Cats</subject><subject>D C motors</subject><subject>Electric motors</subject><subject>Exploitation</subject><subject>Methods</subject><subject>Optimization algorithms</subject><subject>Particle swarm optimization</subject><subject>Phase transitions</subject><subject>Proportional integral derivative</subject><subject>Wolves</subject><issn>1693-6930</issn><issn>2302-9293</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpFkF9LwzAAxIMoOOY-gwGfO_OvSYNPsukcDBTU55C2iWRrk5pkin566yZ4cNzLcQc_AC4xmmMiSXWdTbcLvXc7Pf8gxJE5KTlFJ2BCKCKFJJKeggnmkhaj0TmYpbRFowQipawm4OZZ-xY2OsP0qWMPw5Bd7751dsFDGyJsgs8xdJ3zb_BpvYTOw-UC9iGHeAHOrO6Smf3lFLze370sHorN42q9uN0UDWY8F4I2ohY1FoLxmmnOKcUS1RXWtkUY6ZoS07bMVMZg2ZaSco4bXtqaUWMtIXQKro67Qwzve5Oy2oZ99OOloohVJWJl9dsSx1YTQ0rRWDVE1-v4pTBSB1jqH5Y6wFIHWPQHH-Ff4g</recordid><startdate>20240401</startdate><enddate>20240401</enddate><creator>Aribowo, Widi</creator><creator>Ghith, Ehab Saif</creator><creator>Rahmadian, Reza</creator><creator>Widyartono, Mahendra</creator><creator>Lukita Wardani, Ayusta</creator><creator>Prapanca, Aditya</creator><creator>Laksmi B., Nur Vidia</creator><general>Ahmad Dahlan University</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BVBZV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20240401</creationdate><title>Sand cat swarm optimization for controlling PID in DC motor</title><author>Aribowo, Widi ; Ghith, Ehab Saif ; Rahmadian, Reza ; Widyartono, Mahendra ; Lukita Wardani, Ayusta ; Prapanca, Aditya ; Laksmi B., Nur Vidia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c146t-73c7b7b17746b4a6633190b81afd010ab32edd4e8ee19d593661c65fb43eff223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Cats</topic><topic>D C motors</topic><topic>Electric motors</topic><topic>Exploitation</topic><topic>Methods</topic><topic>Optimization algorithms</topic><topic>Particle swarm optimization</topic><topic>Phase transitions</topic><topic>Proportional integral derivative</topic><topic>Wolves</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aribowo, Widi</creatorcontrib><creatorcontrib>Ghith, Ehab Saif</creatorcontrib><creatorcontrib>Rahmadian, Reza</creatorcontrib><creatorcontrib>Widyartono, Mahendra</creatorcontrib><creatorcontrib>Lukita Wardani, Ayusta</creatorcontrib><creatorcontrib>Prapanca, Aditya</creatorcontrib><creatorcontrib>Laksmi B., Nur Vidia</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>East &amp; South Asia Database</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Telkomnika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aribowo, Widi</au><au>Ghith, Ehab Saif</au><au>Rahmadian, Reza</au><au>Widyartono, Mahendra</au><au>Lukita Wardani, Ayusta</au><au>Prapanca, Aditya</au><au>Laksmi B., Nur Vidia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sand cat swarm optimization for controlling PID in DC motor</atitle><jtitle>Telkomnika</jtitle><date>2024-04-01</date><risdate>2024</risdate><volume>22</volume><issue>2</issue><spage>462</spage><epage>470</epage><pages>462-470</pages><issn>1693-6930</issn><eissn>2302-9293</eissn><abstract>In this article, the direct current (DC) motor control approach is presented using the sand cat swarm optimization (SCSO) method to obtain the best proportional integral derivative (PID) parameters. DC motors are popular equipment. In addition, DC motors are easy to apply. SCSO is a method that adopts the desert cat's life in nature when searching for prey. This cat is able to detect low frequencies below 2 kHz and also has an extraordinary ability to dig for prey. This research was carried out using the MATLAB/Simulink application. To obtain the performance of the SCSO method, a comparison method was used, namely particle swarm optimization (PSO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), and aquila optimizer (AO). From the results of the study, it was found that the settling time value and integral total weighted absolute value error (ITAE) value of the SCSO method were the best compared to other methods.</abstract><cop>Yogyakarta</cop><pub>Ahmad Dahlan University</pub><doi>10.12928/telkomnika.v22i2.25630</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1693-6930
ispartof Telkomnika, 2024-04, Vol.22 (2), p.462-470
issn 1693-6930
2302-9293
language eng
recordid cdi_proquest_journals_3048504582
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Algorithms
Cats
D C motors
Electric motors
Exploitation
Methods
Optimization algorithms
Particle swarm optimization
Phase transitions
Proportional integral derivative
Wolves
title Sand cat swarm optimization for controlling PID in DC motor
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-14T07%3A58%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Sand%20cat%20swarm%20optimization%20for%20controlling%20PID%20in%20DC%20motor&rft.jtitle=Telkomnika&rft.au=Aribowo,%20Widi&rft.date=2024-04-01&rft.volume=22&rft.issue=2&rft.spage=462&rft.epage=470&rft.pages=462-470&rft.issn=1693-6930&rft.eissn=2302-9293&rft_id=info:doi/10.12928/telkomnika.v22i2.25630&rft_dat=%3Cproquest_cross%3E3048504582%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3048504582&rft_id=info:pmid/&rfr_iscdi=true