Applied Design of Optimal PID-with-Feedforward Control Using Two-Parameter Behavior Models

Applied Design of Optimal PID-with-Feedforward Control Using Two-Parameter Behavior Models | 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 Applied Design of Optimal PID-with-Feedforward Control Using Two-Parameter Behavior Models George Smith This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4870157/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 This paper proposes a two-parameter method for adjusting and tuning control systems in order to explore trade-offs in light of real-world objectives and constraints; the user-friendly mechanism draws parallels between robot and human behavior easily understood at an intuitive level. Firstly, the general behavior model enables a designer to adjust weightings in a cost function using two behavior traits, potentially to create 3D plots that illustrate trade-offs. Secondly, the specific behavior model allows someone to explore trade-offs or tune the control gains in real time, for a specific situation or for personal preference, again using only two intuitive behavior traits. Both methods also facilitate a simple strategy of just choosing between a few distinctive behavior types. Moreover, the framework provides a possible model for understanding how some behavior qualities might arise due to the natural constraint of feedback in systems, with implications for programming relatable behaviors into robots. A nonlinear optimal quadratic tracking design for the PID-with-feedforward control of a robot link demonstrates the practical design methodology. Robotics human-computer interaction nonlinear quadratic regulator optimal control robot control PID tuning robot behavior 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. 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-4870157","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336837808,"identity":"ab3c2e94-055c-4b88-8b9f-c5cbfe2d30c3","order_by":0,"name":"George Smith","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDADNgYexgcJpGlh42E2IE0LAxsPmwRRCuXdm499/FJRF80n33us4kGFjRwDe+/jF/i0GJ45ljxb5szh3DY2vrQbCWfSjBl4jptZ4NUyI8eYWbLtAFALj9mNxLbDiQ0SaWwGeLXMfwPU8q8OrKUg8d//eoJa5CV4jBk/NjCDtTAkNhxIYJBIY36AT4sBT1oyM8MxkF9yjCUSjiUbtvEcY8Ong0G+/fBhxh81dbnzm88YfvxRYyfPz97G/AGvLQcYGJh5kEWAVuCPIPkGBgbGH2iC+G0ZBaNgFIyCEQcAbotEvlyELR8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-3417-0810","institution":"","correspondingAuthor":true,"prefix":"","firstName":"George","middleName":"","lastName":"Smith","suffix":""}],"badges":[],"createdAt":"2024-08-06 17:44:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4870157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4870157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61985230,"identity":"4caa7d5a-0ff7-4338-a9d6-fb989cf3caec","added_by":"auto","created_at":"2024-08-08 00:39:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":866917,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4870157/v1_covered_7716aefc-f887-4bdd-911b-39d40fba3d60.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eApplied Design of Optimal PID-with-Feedforward Control Using Two-Parameter Behavior Models\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"human-computer interaction, nonlinear quadratic regulator, optimal control, robot control, PID tuning, robot behavior","lastPublishedDoi":"10.21203/rs.3.rs-4870157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4870157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a two-parameter method for adjusting and tuning control systems in order to explore trade-offs in light of real-world objectives and constraints; 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