Research on Inverse Dynamics modeling of Soft Manipulator

Research on Inverse Dynamics modeling of Soft Manipulator | 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 Research Article Research on Inverse Dynamics modeling of Soft Manipulator Peng Geng, Yanlin He, Yi Yang, Fei Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3872652/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 In recent years, soft manipulators have attracted much attention in the field of robotics research due to their dexterity and good environmental adaptability. However, accurately modeling of soft manipulators remains a difficult task due to the uncertainties in their dynamics. Therefore, this paper proposed an method to accurately model the inverse dynamics of a soft manipulator. A BP neural network model was used to establish the inverse dynamics model of the soft manipulator, and then the particle swarm optimization (PSO) algorithm was employed to optimize the initial weights and biases of the BP neural network, and finally the correspondence between the end position of the soft manipulator and the input air pressure was established. In addition, load weights were introduced as one of the network inputs to enhance the control accuracy of the soft manipulator when it carries a load. By trajectory tracking experiments with and without load, the method was proved to achieve an average end position error of 0.839 mm with a relative error of 0.98%. When carrying loads its average end error is 1.800 mm with a relative error of 2.12%. The results demonstrated that the proposed optimized method can effectively improve the accuracy of the soft manipulator under both no-load and load conditions. Inverse Dynamics Model Soft Robotics Neural Network Particle Swarm Optimization Algorithm 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-3872652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268128786,"identity":"2e7bb116-5a80-46d1-954e-8d975f1b4742","order_by":0,"name":"Peng Geng","email":"","orcid":"","institution":"Beijing Information Science \u0026 Technology University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Geng","suffix":""},{"id":268128787,"identity":"f0947964-d9bc-4efe-a1f3-dcb68129365c","order_by":1,"name":"Yanlin He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACAzBZIMHAL8HYABE6QJQWAwkGyRlALQdI0AJEN2CqCWkxZ+8x/FxgYJFnfLu5+fPHNgY5vhsJjJ8L8Gix7DljLD3DQKLY7M7BNomDbQzGkjcSmKVn4HPYjRwDaR4DicRtNxLbGIBaEjfcSGBj5sGn5f4b498gLZtnJDZ/AGqpJ6zlBo8Z2JYNEokNIIclGBDUciatzBqkZQbIL2fOSRjOPPOwWRqvluOHN9/mqahL7J/d/vhDRZmNPN/x5IOf8WlhYOAwQOZJADEsGeAE7A8IKBgFo2AUjIIRDwAqBk60XpfiFwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Information Science \u0026 Technology University","correspondingAuthor":true,"prefix":"","firstName":"Yanlin","middleName":"","lastName":"He","suffix":""},{"id":268128788,"identity":"bf894d77-26ed-4718-bd26-5355623baf9e","order_by":2,"name":"Yi Yang","email":"","orcid":"","institution":"Beijing Information Science \u0026 Technology University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Yang","suffix":""},{"id":268128789,"identity":"def8da38-99aa-46f5-b2b8-1a04f6794559","order_by":3,"name":"Fei Han","email":"","orcid":"","institution":"Beijing Information Science \u0026 Technology University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-01-17 10:51:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3872652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3872652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52307174,"identity":"58ba3a0b-a0f2-4b70-a41a-3ae92076b446","added_by":"auto","created_at":"2024-03-08 19:46:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1181841,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonInverseDynamicsmodelingofSoftManipulator.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872652/v1_covered_255c1287-91cb-43c8-bb85-7c2da98bceab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Inverse Dynamics modeling of Soft Manipulator","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":"Inverse Dynamics Model, Soft Robotics, Neural Network, Particle Swarm Optimization Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-3872652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3872652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, soft manipulators have attracted much attention in the field of robotics research due to their dexterity and good environmental adaptability. However, accurately modeling of soft manipulators remains a difficult task due to the uncertainties in their dynamics. Therefore, this paper proposed an method to accurately model the inverse dynamics of a soft manipulator. A BP neural network model was used to establish the inverse dynamics model of the soft manipulator, and then the particle swarm optimization (PSO) algorithm was employed to optimize the initial weights and biases of the BP neural network, and finally the correspondence between the end position of the soft manipulator and the input air pressure was established. In addition, load weights were introduced as one of the network inputs to enhance the control accuracy of the soft manipulator when it carries a load. By trajectory tracking experiments with and without load, the method was proved to achieve an average end position error of 0.839 mm with a relative error of 0.98%. When carrying loads its average end error is 1.800 mm with a relative error of 2.12%. 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