Adaptive Passive Control for a Lower Limb Rehabilitation Robot Based on Controller Gain Tuning

Adaptive Passive Control for a Lower Limb Rehabilitation Robot Based on Controller Gain Tuning | 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 Adaptive Passive Control for a Lower Limb Rehabilitation Robot Based on Controller Gain Tuning Yan Yang¹, Minghao Zhang², Yu Liu¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7572353/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 Background: The inherent compliance of cable-driven lower limb rehabilitation robots, though advantageous for safe human-robot interaction, can lead to unsatisfactory trajectory tracking performance when control strategies are inadequately designed, hindering their effectiveness in clinical rehabilitation. Methods: This study presents an adaptive passive control strategy based on online adjustment of controller gains. A dynamics model of the robot was established using Lagrange’s equations. A gain-adaptive iterative learning control algorithm was proposed, and its stability was rigorously proven via Lyapunov analysis. Reference trajectories for the hip and knee joints were generated from captured human gait data. The method was validated through extensive simulations and experiments on a self-developed rehabilitation robot prototype. Results: Simulation results demonstrated that the proposed method achieves convergence nearly five times faster than conventional iterative algorithms under various disturbances, with mean tracking errors of 1.24% for the hip and 0.321% for the knee. Experimental results on the physical system showed an average root mean square error of 0.166 cm, a mean absolute error of 0.134 cm, a maximum error of 0.396 cm, and a tracking percentage error of 5.62%. Conclusions: The proposed control method significantly improves trajectory tracking accuracy in cable-driven lower limb rehabilitation robots, showing strong potential for application in clinical rehabilitation settings. The method enhances tracking precision and convergence speed, indicating its value for supporting recovery in patients with motor impairments. Trajectory tracking Gain-adaptive iterative learning control Lower limb rehabilitation robot Cable-driven robot Adaptive control Lyapunov stability 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-7572353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523932323,"identity":"9b363346-927a-4e14-a7d9-422599fac231","order_by":0,"name":"Yan Yang¹","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yang¹","suffix":""},{"id":523932324,"identity":"a34d327a-9690-402a-9060-17c768a1ca65","order_by":1,"name":"Minghao 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Tuning","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":"Trajectory tracking, Gain-adaptive iterative learning control, Lower limb rehabilitation robot, Cable-driven 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A dynamics model of the robot was established using Lagrange’s equations. A gain-adaptive iterative learning control algorithm was proposed, and its stability was rigorously proven via Lyapunov analysis. Reference trajectories for the hip and knee joints were generated from captured human gait data. The method was validated through extensive simulations and experiments on a self-developed rehabilitation robot prototype.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eSimulation results demonstrated that the proposed method achieves convergence nearly five times faster than conventional iterative algorithms under various disturbances, with mean tracking errors of 1.24% for the hip and 0.321% for the knee. Experimental results on the physical system showed an average root mean square error of 0.166 cm, a mean absolute error of 0.134 cm, a maximum error of 0.396 cm, and a tracking percentage error of 5.62%.\u003c/p\u003e\n\u003cp\u003eConclusions:\u003c/p\u003e\n\u003cp\u003eThe proposed control method significantly improves trajectory tracking accuracy in cable-driven lower limb rehabilitation robots, showing strong potential for application in clinical rehabilitation settings. The method enhances tracking precision and convergence speed, indicating its value for supporting recovery in patients with motor impairments.\u003c/p\u003e","manuscriptTitle":"Adaptive Passive Control for a Lower Limb Rehabilitation Robot Based on Controller Gain Tuning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 17:46:27","doi":"10.21203/rs.3.rs-7572353/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":"7e171b17-ef36-40ce-881b-cf77df9dfe3f","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T19:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 17:46:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7572353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7572353","identity":"rs-7572353","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}