Adaptive Control Methods for Interacting Conical Tank Processes Using Optimization Algorithm

Adaptive Control Methods for Interacting Conical Tank Processes Using Optimization Algorithm | 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 Article Adaptive Control Methods for Interacting Conical Tank Processes Using Optimization Algorithm K. Arulselvan, T. Suresh Padmanabhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6625198/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 The conical tanks are a nonlinear system that are used widely in different industries. The process of maintaining the levels of the tank is very complex and always controlled through the controller unit. The most common controller units are built with Proportional-Integral-Derivative (PID) or Proportional-Integral (PI) controllers. The improper tuning of PI controllers provides inadequate results and the system is subjected to settling time and overshoot issues. In the present work, the Optimized gain schedule PI controller is established to effectively control the nonlinear conical tanks. The tuning parameters of PI controller are optimized using the novel Hybrid optimization algorithm, which integrates the Particle Swarm algorithm (PSO) and the Golden Jackal (GJ) algorithm. The system is simulated using MATLAB. The set point and two regions are established, and the simulation is carried out to measure the step response and liquid level over the period. The rising time, settling time for the coupled tank system is identified to be 0.2197 s and 0.3912 s respectively. The optimized GSCPI (OGSCPI) controller performance is compared to the PSO based GSCPI and GJO based GSCPI controllers. The proposed OGSCPI controller outperformed the other controller and was found to be effective. Biological sciences/Biochemistry Physical sciences/Engineering Nonlinear Interacting conical tank Gain Scheduling controller-based PI controller Particle Swarm Optimization Golden Jackal Optimization 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-6625198","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466281465,"identity":"401f7c26-de5e-4ae9-8940-4642809f7fbe","order_by":0,"name":"K. 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