Robotics and Control: A Mechanical Engineering Perspective

Robotics and Control: A Mechanical Engineering Perspective | 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PDF Research Article Robotics and Control: A Mechanical Engineering Perspective Sourbh Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9519145/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 As we Know Robotics is at the intersection of mechanical engineering, electrical systems, and intelligent con- trol, driving innovation in industrial automation, autonomous vehicles, and healthcare systems. This research focuses on the mechanical engineering aspects of robotics, emphasizing the modeling, dynamics, and control of robotic manipulators and mobile platforms. Classical control approaches such as Propor- tional–Integral–Derivative (PID) control and Linear Quadratic Regulator (LQR) control, along with modern approaches like Model Predictive Control (MPC) and adaptive control, are reviewed to evaluate their performance in trajectory tracking and disturbance rejection. Mathematical formulations based on rigid-body dynamics, state-space representation, and kinematic analysis establish the theoretical foundation. The Simulations in MATLAB/Simulink compare controller performance across different robotic systems. Results reveal the trade-offs between simplicity, robustness, and adaptability: while PID is effective for basic tasks, MPC offers superior robustness and predictive capabilities for complex applications. The study on the Topic concludes by highlighting industrial, autonomous, and medical applications, and suggests future research directions including reinforcement learning-based adaptive control and digital twin integration with precision. Robotics Robotics Proportional–Integral–Derivative (PID) Linear Quadratic Regulator (LQR) Model Predictive Control (MPC) Rigid-Body Dynamics Full Text Additional Declarations The authors declare no competing interests. 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. 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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-9519145","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629129284,"identity":"35b4c8ee-0610-439f-8060-144b1851f168","order_by":0,"name":"Sourbh Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYJACZsaGBDDD4EMFiMvcQLyWwhlnIFzitXzmbQNRBLTIt/eYfS7ckZa4tv3sw40z59VG87cDtfyo2IZTi8GZM8azZ57JSdx2Jt3Y4OO247kzDjM2MPacuY1bi0SOMTNvW0XitgNpbIYztx3LbQBqYWZsw61FfgZMy/ln7L955xzLnU9IC8MNsBagw26kMRjzNtTkbiCkxeDMsWLmmW1pxttuPGMwnHHsQO5GoJaD+Pwi3968mbmwLVl22/k0YFTW1OXOO3/44IMfFXgchgYOg8kDRKsHgjpSFI+CUTAKRsEIAQBptGF8OD/h7QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-9229-2815","institution":"IIT (ISM) Dhanbad","correspondingAuthor":true,"prefix":"","firstName":"Sourbh","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2026-04-24 16:06:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9519145/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9519145/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107885062,"identity":"19d4ab94-69bb-48ea-be49-a9ce348bf8e0","added_by":"auto","created_at":"2026-04-27 09:19:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":447633,"visible":true,"origin":"","legend":"","description":"","filename":"ID71SourbhKumar.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9519145/v1_covered_63652562-aab1-457d-8499-7aad0bcb5665.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRobotics and Control: A Mechanical Engineering Perspective\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"IIT (ISM) Dhanbad","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":"Robotics, Proportional–Integral–Derivative (PID), Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), Rigid-Body Dynamics","lastPublishedDoi":"10.21203/rs.3.rs-9519145/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9519145/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs we Know Robotics is at the intersection of mechanical engineering, electrical systems, and intelligent con- trol, driving innovation in industrial automation, autonomous vehicles, and healthcare systems. 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