Intelligent nonlinear predictive control for wheeled mobile robot on uncertain path

Intelligent nonlinear predictive control for wheeled mobile robot on uncertain path | 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 Intelligent nonlinear predictive control for wheeled mobile robot on uncertain path mostafa jalalnezhad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3738224/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Aug, 2024 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted 4 You are reading this latest preprint version Abstract In this paper, the weak points have been investigated, the proposal to solve them, the application of the solution and the comparison of the results in the mode of simulation and experimental testing have been discussed. For this purpose, a four-wheel mobile robot has been considered for simulation and implementation, and a linear quadratic regulator controller and a nonlinear model predictive controller have been used to control it. But the combination of these classic and modern controllers with machine learning can greatly help to make these controllers work more accurately; As a result, in order to increase the accuracy of the performance of these controllers, by training neural networks of multilayer perceptrons, the controllers have been made intelligent. Controllers with cost function have coefficients as weighting to the matrix of system state variables and control input, which are greatly affected by changing these two weighting matrices of problem solving and optimization. For this reason, it is necessary to extract these two matrices for each separate path in order to improve the performance of the controller by trial and error. But by applying the proposed network which is trained with a new algorithm, not only the performance accuracy has increased, but the network extracts these two matrices without the need to spend human energy. Also, in order to reduce the existing time delays, especially in the implementation of the nonlinear controller on the robot in the experimental mode, by training other neural networks to optimally extract the benefit of the forecast horizon, reducing the calculations and increasing the speed of the solution has been achieved. In the hardware part, by examining and using operators such as the pixy camera and the U2D2 interface, which are faster than the usual method, the solution time has been reduced. wheeled mobile robot reduction of computational time delay intelligent controller predictive control of nonlinear model neural networks Full Text Cite Share Download PDF Status: Published Journal Publication published 11 Aug, 2024 Read the published version in Journal of the Brazilian Society of Mechanical Sciences and Engineering → Version 1 posted Reviewers agreed at journal 01 Jan, 2024 Reviewers invited by journal 01 Jan, 2024 Editor assigned by journal 10 Dec, 2023 First submitted to journal 03 Dec, 2023 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. 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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-3738224","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264653157,"identity":"1718f92c-7bd4-44e1-b06f-16774e88fe1a","order_by":0,"name":"mostafa jalalnezhad","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-6304-9894","institution":"Kharazmi University","correspondingAuthor":true,"prefix":"","firstName":"mostafa","middleName":"","lastName":"jalalnezhad","suffix":""}],"badges":[],"createdAt":"2023-12-11 11:27:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3738224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3738224/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40430-024-05107-2","type":"published","date":"2024-08-11T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62298743,"identity":"21fa2d9c-677d-4d79-a0c1-53819364e82c","added_by":"auto","created_at":"2024-08-12 16:16:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1098751,"visible":true,"origin":"","legend":"","description":"","filename":"mainpaper241.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3738224/v1_covered_a9c3225f-97b6-4d40-aa28-455e4e2ac210.pdf"}],"financialInterests":"","formattedTitle":"Intelligent nonlinear predictive control for wheeled mobile robot on uncertain path","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-brazilian-society-of-mechanical-sciences-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmse","sideBox":"Learn more about [Journal of the Brazilian Society of Mechanical Sciences and Engineering](http://link.springer.com/journal/40430)","snPcode":"40430","submissionUrl":"https://www.editorialmanager.com/bmse/default2.aspx","title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"wheeled mobile robot, reduction of computational time delay, intelligent controller, predictive control of nonlinear model, neural networks","lastPublishedDoi":"10.21203/rs.3.rs-3738224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3738224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, the weak points have been investigated, the proposal to solve them, the application of the solution and the comparison of the results in the mode of simulation and experimental testing have been discussed. For this purpose, a four-wheel mobile robot has been considered for simulation and implementation, and a linear quadratic regulator controller and a nonlinear model predictive controller have been used to control it. But the combination of these classic and modern controllers with machine learning can greatly help to make these controllers work more accurately; As a result, in order to increase the accuracy of the performance of these controllers, by training neural networks of multilayer perceptrons, the controllers have been made intelligent. Controllers with cost function have coefficients as weighting to the matrix of system state variables and control input, which are greatly affected by changing these two weighting matrices of problem solving and optimization. For this reason, it is necessary to extract these two matrices for each separate path in order to improve the performance of the controller by trial and error. But by applying the proposed network which is trained with a new algorithm, not only the performance accuracy has increased, but the network extracts these two matrices without the need to spend human energy. Also, in order to reduce the existing time delays, especially in the implementation of the nonlinear controller on the robot in the experimental mode, by training other neural networks to optimally extract the benefit of the forecast horizon, reducing the calculations and increasing the speed of the solution has been achieved. In the hardware part, by examining and using operators such as the pixy camera and the U2D2 interface, which are faster than the usual method, the solution time has been reduced.\u003c/p\u003e","manuscriptTitle":"Intelligent nonlinear predictive control for wheeled mobile robot on uncertain path","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 02:49:37","doi":"10.21203/rs.3.rs-3738224/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-02T00:01:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-01T23:54:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-11T01:58:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","date":"2023-12-03T13:47:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-brazilian-society-of-mechanical-sciences-and-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmse","sideBox":"Learn more about [Journal of the Brazilian Society of Mechanical Sciences and Engineering](http://link.springer.com/journal/40430)","snPcode":"40430","submissionUrl":"https://www.editorialmanager.com/bmse/default2.aspx","title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ea2aeb3c-7c51-4551-b703-bc342b62ccb2","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T16:07:59+00:00","versionOfRecord":{"articleIdentity":"rs-3738224","link":"https://doi.org/10.1007/s40430-024-05107-2","journal":{"identity":"journal-of-the-brazilian-society-of-mechanical-sciences-and-engineering","isVorOnly":false,"title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering"},"publishedOn":"2024-08-11 15:57:11","publishedOnDateReadable":"August 11th, 2024"},"versionCreatedAt":"2024-01-03 02:49:37","video":"","vorDoi":"10.1007/s40430-024-05107-2","vorDoiUrl":"https://doi.org/10.1007/s40430-024-05107-2","workflowStages":[]},"version":"v1","identity":"rs-3738224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3738224","identity":"rs-3738224","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}