Gas Turbine Speed Controller Based on Improved Circle-SCA-BSO Algorithm

Gas Turbine Speed Controller Based on Improved Circle-SCA-BSO Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Gas Turbine Speed Controller Based on Improved Circle-SCA-BSO Algorithm Yue Dong, Zixiao Wang, Lina Zhang, Xinxiang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6624721/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Gas turbine is a kind of dual-purpose rotating thermal machinery, widely used in power generation, shipbuilding and aviation power, etc., with the advantages of high efficiency, fast start and low emissions. In this study, an improved Circle-SCA-BSO algorithm (IC-SCA-BSO) is proposed to optimize PID parameters to ease the complicated parameter setting of gas turbine controller. Optimization of beetle swarm optimization algorithm (BSO) usually comes with slow convergence speed, low accuracy and prone to fall into local optimum, hereby our optimization is carried out from three aspects: population initialization, optimization weight and learning factors. First, a uniformly distributed circle mapping is utilized for population initialization. Second, the nonlinear decreasing idea is employed for weight optimization. Considering characteristics of global search in the early stage and local development in the later stage of algorithm optimization, the nonlinear decreasing function expression is designed. Third, combined with the sine cosine algorithm (SCA), the sine and cosine factors are introduced into the learning factors and combined with the nonlinear decreasing coefficient to make the learning factors show a trend of oscillatory attenuation in the set interval. According to probability p, sine or cosine factor is switched as the learning factors. The optimized PID controller and other four controllers are compared by tracking test and anti-interference test. The test results show that IC-SCA-BSO-PID yields faster response, higher steady-state accuracy and stronger anti-interference control effect, which is significantly outperformings the other four controllers. Physical sciences/Energy science and technology Physical sciences/Engineering gas turbine speed control PID controller optimization improved BSO algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Aug, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 28 May, 2025 Editor invited by journal 23 May, 2025 Submission checks completed at journal 23 May, 2025 First submitted to journal 08 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6624721","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477693887,"identity":"d33ed33f-8d50-48fd-8a5a-838b55f3bac3","order_by":0,"name":"Yue Dong","email":"","orcid":"","institution":"Communication University of China","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Dong","suffix":""},{"id":477693888,"identity":"62f85272-9e67-47b9-9ec7-c2b4dbba3f43","order_by":1,"name":"Zixiao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYNACAwYeBgbmAxDOAeK1sCWQogUMeAyI02Jw/Ozhlz8KDsvwt/d8/MxTwyDHdyOB8XMBPi1n8tIsJAzSeCTOnN0szXOMwVjyRgKz9Aw8WswO5JgZGBjY8DDcyN3GzNvAkLjhRgIbMw8+LeffmBkkGEjwyN/IeQbSUk9Yy40c4wcHgLYY3MhhA2lJMCCkxf7GGzPGBqBfDM8cM5acc0zCcOaZh83S+LRI9ucYf/zx57C93PHmhx/e1NjI8x1PPvgZnxYgYJNA4oDYjA34NQATygdCKkbBKBgFo2CEAwCmAUiva+DKsAAAAABJRU5ErkJggg==","orcid":"","institution":"Communication University of China","correspondingAuthor":true,"prefix":"","firstName":"Zixiao","middleName":"","lastName":"Wang","suffix":""},{"id":477693889,"identity":"31a2e377-9a3d-43ff-973d-001240e8996c","order_by":2,"name":"Lina Zhang","email":"","orcid":"","institution":"Shenyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Zhang","suffix":""},{"id":477693890,"identity":"8eb03c02-1b24-4c2f-9379-854523c63348","order_by":3,"name":"Xinxiang Zhang","email":"","orcid":"","institution":"Southern Methodist University","correspondingAuthor":false,"prefix":"","firstName":"Xinxiang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-09 03:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6624721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6624721/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-37087-9","type":"published","date":"2026-01-21T15:58:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101152441,"identity":"cad99b4f-3586-4f07-9119-2f190381ab09","added_by":"auto","created_at":"2026-01-26 16:11:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1221345,"visible":true,"origin":"","legend":"","description":"","filename":"GasTurbineSpeedControllerBasedonImprovedCircleSCABSOAlgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6624721/v1_covered_93bae31c-2603-461b-93c0-e6decfebed9a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gas Turbine Speed Controller Based on Improved Circle-SCA-BSO Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gas turbine, speed control, PID controller optimization, improved BSO algorithm","lastPublishedDoi":"10.21203/rs.3.rs-6624721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6624721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGas turbine is a kind of dual-purpose rotating thermal machinery, widely used in power generation, shipbuilding and aviation power, etc., with the advantages of high efficiency, fast start and low emissions. In this study, an improved Circle-SCA-BSO algorithm (IC-SCA-BSO) is proposed to optimize PID parameters to ease the complicated parameter setting of gas turbine controller. Optimization of beetle swarm optimization algorithm (BSO) usually comes with slow convergence speed, low accuracy and prone to fall into local optimum, hereby our optimization is carried out from three aspects: population initialization, optimization weight and learning factors. First, a uniformly distributed circle mapping is utilized for population initialization. Second, the nonlinear decreasing idea is employed for weight optimization. Considering characteristics of global search in the early stage and local development in the later stage of algorithm optimization, the nonlinear decreasing function expression is designed. 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