Design of a surgical robot servo control system based on RBF neural network adaptive PID

Design of a surgical robot servo control system based on RBF neural network adaptive PID | 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 Design of a surgical robot servo control system based on RBF neural network adaptive PID Panpan Meng, Xiaoyi Jin, Yibo Niu, Weibiao Kong, Ying Xi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5232481/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents the design of a servo control system for flexible endoscope surgical robots. For the system with a certain degree of uncertainty, an Radial Basis Function (RBF) neural network adaptive Proportional-Integral-Derivative (PID) control algorithm is proposed. This algorithm fully utilizes the adaptivity, self-learning ability, and excellent nonlinear approximation ability of RBF neural network to achieve real-time online adjustment of PID. We developed a mathematical model and transfer function of the servo and conducted a detailed simulation study with the help of MATLAB platform. The simulation results show that the RBF neural network adaptive PID control algorithm not only significantly outperforms the traditional PID controller in terms of performance, demonstrating stronger adaptability and anti-interference ability, but also greatly improves the control effect for nonlinear time-varying systems. We establish the block diagram of the servo control system on the LABVIEW platform and further confirmed the superiority of the algorithm in practical applications through experimental verification. This is specifically manifested in the effective reduction of system overshooting and the significant improvement of response speed and control accuracy. Physical sciences/Engineering/Mechanical engineering Biological sciences/Neuroscience/Motor control RBF neural network adaptive PID control nonlinear control servo system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5232481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376991498,"identity":"a33524b1-3bd8-4a76-af05-635d4d632ef3","order_by":0,"name":"Panpan Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACefbGhoMfeGzk2OQfHyBOi2HP4YOPJWTSjPkY0hKItOZGWrIBj83hRDmGHAPidDA25JhJSOQcTmBjOPPxxhsGOzndBgJa2BnOmEkUnEnPY2Ps3Ww5hyHZ2OwAIVsae8wkJHusi9mYebdJ8zAcSNxGSAvDYR4zCd5/zIltbDzPiNRyjA3ofR7nxDYeHjbitBj2MAMDmSfNmE2CzdhyjgERfpGXfwiJSvkZzA9vvKmwkyOoBQVI8BAZNchaSNUxCkbBKBgFIwIAAO7dPe3K+py4AAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Engineering Science","correspondingAuthor":true,"prefix":"","firstName":"Panpan","middleName":"","lastName":"Meng","suffix":""},{"id":376991499,"identity":"dc08b70b-9cc6-4e3c-97ab-19c3988d58ee","order_by":1,"name":"Xiaoyi Jin","email":"","orcid":"","institution":"Shanghai University of Engineering Science","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Jin","suffix":""},{"id":376991500,"identity":"3c50ffae-8231-40b3-9250-eb226fc4adf2","order_by":2,"name":"Yibo Niu","email":"","orcid":"","institution":"Shanghai University of Engineering Science","correspondingAuthor":false,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Niu","suffix":""},{"id":376991501,"identity":"5143b798-52bb-4194-83c8-3f208203f3f0","order_by":3,"name":"Weibiao Kong","email":"","orcid":"","institution":"Zhongke Intelligent (Wuxi) Technology Co, general manager's office","correspondingAuthor":false,"prefix":"","firstName":"Weibiao","middleName":"","lastName":"Kong","suffix":""},{"id":376991502,"identity":"ddc1b350-96f3-4046-a8cb-1649bd9347f5","order_by":4,"name":"Ying Xi","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Xi","suffix":""}],"badges":[],"createdAt":"2024-10-09 12:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5232481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5232481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72369088,"identity":"2c95e49d-09e8-4e5f-9418-8fa430d6e053","added_by":"auto","created_at":"2024-12-26 07:24:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":752710,"visible":true,"origin":"","legend":"","description":"","filename":"DesignofasurgicalrobotservocontrolsystembasedonRBFneuralnetworkadaptivePID.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5232481/v1_covered_89fe2ed7-caa9-4ab2-bad3-6f2eb3a43bf0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design of a surgical robot servo control system based on RBF neural network adaptive PID","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"RBF neural network, adaptive PID control, nonlinear control, servo system","lastPublishedDoi":"10.21203/rs.3.rs-5232481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5232481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents the design of a servo control system for flexible endoscope surgical robots. For the system with a certain degree of uncertainty, an Radial Basis Function (RBF) neural network adaptive Proportional-Integral-Derivative (PID) control algorithm is proposed. This algorithm fully utilizes the adaptivity, self-learning ability, and excellent nonlinear approximation ability of RBF neural network to achieve real-time online adjustment of PID. We developed a mathematical model and transfer function of the servo and conducted a detailed simulation study with the help of MATLAB platform. The simulation results show that the RBF neural network adaptive PID control algorithm not only significantly outperforms the traditional PID controller in terms of performance, demonstrating stronger adaptability and anti-interference ability, but also greatly improves the control effect for nonlinear time-varying systems. We establish the block diagram of the servo control system on the LABVIEW platform and further confirmed the superiority of the algorithm in practical applications through experimental verification. This is specifically manifested in the effective reduction of system overshooting and the significant improvement of response speed and control accuracy.\u003c/p\u003e","manuscriptTitle":"Design of a surgical robot servo control system based on RBF neural network adaptive PID","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-15 12:39:46","doi":"10.21203/rs.3.rs-5232481/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"caaa8086-50bf-42ef-8989-b9e64bb410d2","owner":[],"postedDate":"November 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40130567,"name":"Physical sciences/Engineering/Mechanical engineering"},{"id":40130568,"name":"Biological sciences/Neuroscience/Motor control"}],"tags":[],"updatedAt":"2024-12-26T07:23:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-15 12:39:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5232481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5232481","identity":"rs-5232481","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}