Adaptive dynamic programming path tracking control of a tractor- trailer wheeled mobile robot

preprint OA: closed
Full text JSON View at publisher
AI-generated deep summary by claude@2026-06, 2026-06-24 · read from full text

This paper studies adaptive dynamic programming control for a tractor-trailer wheeled mobile robot with complex nonlinear dynamics, aiming to achieve continuous-time path tracking (and velocity tracking) despite uncertainties. Using a critical neural network to approximate the optimal cost function, the authors develop decoupled kinematic and dynamic ADP controllers defined with tracking error signals for each loop, and they model the robot dynamics in an affine form. Theoretical results are presented proving closed-loop stability and convergence, and simulations report improved tracking performance over prior techniques with lower tracking errors and control effort. The work is a preprint and the summary provided here relies on the abstract’s stated validation rather than detailed experimental context. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Full text 9,729 characters · extracted from preprint-html · click to expand
Adaptive dynamic programming path tracking control of a tractor- trailer wheeled mobile robot | 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 dynamic programming path tracking control of a tractor- trailer wheeled mobile robot Aliakbar Ghasemzadeh, Roya Amjadifard, Ali Keymasi-Khalaji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3863165/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 Tractor-trailer wheeled mobile robots (TTWMRs) have complex nonlinear dynamics that make their precise trajectory tracking control challenging. This paper investigates an adaptive dynamic programming (ADP) approach using a critical neural network (NN) to address the tracking control for continuous-time TTWMR that is modeled in a suitable affine form. A critical NN approximates the optimal cost function and enables adaptive tuning of the control policy. Decoupled kinematic and dynamic ADP controllers are proposed for integrated path and velocity tracking. For this purpose, tracking error signals are defined for each control loop. Theoretical analysis proves closed-loop stability and convergence. Simulations demonstrate superior tracking performance compared to previous techniques, with lower errors and control efforts. This highlights the benefits of ADP for optimizing TTWMR control despite uncertainties. The adaptive optimal control enables promising capabilities for autonomous applications. Tractor-trailer wheeled mobile robot path tracking adaptive dynamic programming and actor-critic neural network 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-3863165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267321909,"identity":"77ca1b54-61d5-443b-8424-c92b8533b836","order_by":0,"name":"Aliakbar Ghasemzadeh","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Aliakbar","middleName":"","lastName":"Ghasemzadeh","suffix":""},{"id":267321910,"identity":"5db0d539-265e-45cb-8e1e-7a757165bd24","order_by":1,"name":"Roya Amjadifard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHCCBAYeAzkgnXyYGSLA2ECMFmMgnZZMtBYGBh4GkJYcY2aiXMU/7cDDD28KDOTN23M+Gxe2McjzNzC3fcCnReJ2QrLkHAMDwzln3m5OntnGYDjjAGPzDLzW3E5IkOYx+MM4QyJ382HeNgbGDQyMzXh1yANt+c1jYGA/QyLnMUiLPUEtBrcT0oC2GCQCtTAnA7UkEtRiCNRiCfRL8gyeZ8bGPOckkmccJqBF7nZO8o03fwxsZ7AnP5bmKbOx7W9vf4xXCzBSEpB5EgwMhGOH/QBBJaNgFIyCUTDCAQAIqUQAGIHh3wAAAABJRU5ErkJggg==","orcid":"","institution":"Kharazmi University","correspondingAuthor":true,"prefix":"","firstName":"Roya","middleName":"","lastName":"Amjadifard","suffix":""},{"id":267321911,"identity":"2b49d5e6-4c7f-445d-81c5-2397e67bb0b2","order_by":2,"name":"Ali Keymasi-Khalaji","email":"","orcid":"","institution":"Kharazmi University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Keymasi-Khalaji","suffix":""}],"badges":[],"createdAt":"2024-01-14 11:59:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3863165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3863165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56040189,"identity":"4337c5c8-0726-4181-b091-b3e67d1a6007","added_by":"auto","created_at":"2024-05-07 19:14:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":787681,"visible":true,"origin":"","legend":"","description":"","filename":"journal1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3863165/v1_covered_c9db075f-e584-4773-9eac-3f73c62f6ee7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive dynamic programming path tracking control of a tractor- trailer wheeled mobile robot","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":"Tractor-trailer wheeled mobile robot, path tracking, adaptive dynamic programming, and actor-critic neural network","lastPublishedDoi":"10.21203/rs.3.rs-3863165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3863165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTractor-trailer wheeled mobile robots (TTWMRs) have complex nonlinear dynamics that make their precise trajectory tracking control challenging. This paper investigates an adaptive dynamic programming (ADP) approach using a critical neural network (NN) to address the tracking control for continuous-time TTWMR that is modeled in a suitable affine form. A critical NN approximates the optimal cost function and enables adaptive tuning of the control policy. Decoupled kinematic and dynamic ADP controllers are proposed for integrated path and velocity tracking. For this purpose, tracking error signals are defined for each control loop. Theoretical analysis proves closed-loop stability and convergence. Simulations demonstrate superior tracking performance compared to previous techniques, with lower errors and control efforts. This highlights the benefits of ADP for optimizing TTWMR control despite uncertainties. The adaptive optimal control enables promising capabilities for autonomous applications.\u003c/p\u003e","manuscriptTitle":"Adaptive dynamic programming path tracking control of a tractor- trailer wheeled mobile robot","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-17 07:12:25","doi":"10.21203/rs.3.rs-3863165/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":"74b6559a-e077-49ef-80cc-708715965696","owner":[],"postedDate":"January 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-07T19:07:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-17 07:12:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3863165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3863165","identity":"rs-3863165","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: preprint-html

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00