PID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver - RIOTS

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dc.authoridChen, YangQuan/0000-0002-7422-5988
dc.authoridATES, Abdullah/0000-0002-4236-6794
dc.authorwosidChen, YangQuan/A-2301-2008
dc.authorwosidYuan, Jie/AAD-1604-2019
dc.authorwosidZhao, Tiebiao/AAI-8457-2020
dc.authorwosidATES, Abdullah/V-6929-2018
dc.contributor.authorDehghan, Sina
dc.contributor.authorZhao, Tiebiao
dc.contributor.authorZhao, Yang
dc.contributor.authorYuan, Jie
dc.contributor.authorAtes, Abdullah
dc.contributor.authorChen, YangQuan
dc.date.accessioned2024-08-04T20:44:36Z
dc.date.available2024-08-04T20:44:36Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description3rd IFAC Conference on Advances in Proportional-Integral-Derivative Control (PID) -- MAY 09-11, 2018 -- Ghent Univ, Ghent, BELGIUMen_US
dc.description.abstractThis paper presents a multi-variable Model Predictive Control (MPC) based controller for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge. This model represents a two-input, two-output system with strong nonlinearities and high coupling between its variables. A general purpose optimal control problem (OCP) solver Matlab toolbox called RIOTS is used as the OCP solver for the proposed MPC scheme which allows for straightforward implementation of the method and for solving a wide range of constrained linear and nonlinear optimal control problems. A conditional integral (CI) compensator is embedded in the controller to compensate for the small steady state errors. This method shows significant improvements in performance compared to both discrete decentralized control (C1) and multi-variable PID controller (C2) originally given in PID2018 Benchmark Challenge as a baseline. Our solution is introduced in detail in this paper and our final results using the overall relative index, J, are 0.2 over Cl and 0.3 over C2, respectively. In other words, we achieved 80% improvement over Cl and 70% improvement over C2. We expect to achieve further improvements when some optimized searching efforts are used for MPC and CI parameter tuning. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipInt Federat Automat Control, Tech Comm Control Design 2 1,Int Federat Automat Control, Tech Comm Proc Control 6 1,Int Federat Automat Control, Tech Comm Control Educ 9 4en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK-BIDEP) [2214/A]; China Scholarship Councilen_US
dc.description.sponsorshipA. Ates is supported by The Scientific and Technological Research Council of Turkey (TUBITAK-BIDEP) with 2214/A program number. Y. Zhao and J. Yuan are supported by China Scholarship Council.en_US
dc.identifier.doi10.1016/j.ifacol.2018.06.112
dc.identifier.endpage887en_US
dc.identifier.issn2405-8963
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85048831484en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage882en_US
dc.identifier.urihttps://doi.org/10.1016/j.ifacol.2018.06.112
dc.identifier.urihttps://hdl.handle.net/11616/98354
dc.identifier.volume51en_US
dc.identifier.wosWOS:000435709300151en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofIfac Papersonlineen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectModel predictive Controlen_US
dc.subjectRIOTSen_US
dc.subjectOptimal Control Problems Solveren_US
dc.subjectPID2018 Benchmark Challengeen_US
dc.subjectperformance improvementen_US
dc.titlePID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver - RIOTSen_US
dc.typeConference Objecten_US

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