{"id":{"repo_id":"queens","oai_identifier":"oai:queensu.scholaris.ca:1974/12286"},"canonical_url":"https://search.dev.ndltd.org/etd/queens/oai:queensu.scholaris.ca:1974/12286","repository":{"repo_id":"queens","name":"Queens University","base_url":"https://qspace.library.queensu.ca/server/oai/request"},"display":{"title":"Integration of Extremum Seeking and Model Predictive Control for Discrete Time Systems","abstract":"This thesis considers a time-varying extremum seeking control algorithm that adjusts set-points provided to a model predictive controller for a vapour compression system. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates---a problem emphasized in application by the long time constants associated with thermal systems. The proposed method uses time-varying extremum seeking, which has faster and more reliable convergence properties for this application. In particular, we regulate the compressor discharge temperature using a model predictive controller with set-points selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of real-time optimization), and use discrete-time extremum seeking control to drive these set-points to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Further, because the required cooling capacity (and therefore compressor speed) is a function of measured and unmeasured disturbances, the optimal compressor discharge temperature set-point must vary according to these conditions. We show that the energy optimal discharge temperature is tracked with the time-varying extremum seeking algorithm in the presence of disturbances.","abstract_html":"This thesis considers a time-varying extremum seeking control algorithm that adjusts set-points provided to a model predictive controller for a vapour compression system. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates---a problem emphasized in application by the long time constants associated with thermal systems. The proposed method uses time-varying extremum seeking, which has faster and more reliable convergence properties for this application. In particular, we regulate the compressor discharge temperature using a model predictive controller with set-points selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of real-time optimization), and use discrete-time extremum seeking control to drive these set-points to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Further, because the required cooling capacity (and therefore compressor speed) is a function of measured and unmeasured disturbances, the optimal compressor discharge temperature set-point must vary according to these conditions. We show that the energy optimal discharge temperature is tracked with the time-varying extremum seeking algorithm in the presence of disturbances.","abstract_has_math":false,"creators":["Weiss, Walter"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Chemical Engineering","school":null,"contributors":[],"advisors":["Guay, Martin"],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-07-16","date_published":"2014-07-16","updated_at":"2026-07-27T20:35:33Z","subjects":["MPC","Real Time Optimization","Vapour Compression System","ESC","Compressor Discharge Temperature","VCS","Extremum Seeking Control","Time-Varying Extremum Seeking Control","Discrete-Time","Model Predictive Control"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1974/12286","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Chemical Engineering"]},{"key":"dc:contributor.supervisor","label":"Supervisor","values":["Guay, Martin"]},{"key":"dc:creator","label":"Author","values":["Weiss, Walter"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-07-16 13:42:57.628"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-07-16T18:46:41Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-07-16T18:46:41Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-07-16"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["MPC","Real Time Optimization","Vapour Compression System","ESC","Compressor Discharge Temperature","VCS","Extremum Seeking Control","Time-Varying Extremum Seeking Control","Discrete-Time","Model Predictive Control"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1974/12286"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (Master, Chemical Engineering) -- Queen's University, 2014-07-16 13:42:57.628"]},{"key":"dc:description.abstract","label":"Abstract","values":["This thesis considers a time-varying extremum seeking control algorithm that adjusts set-points provided to a model predictive controller for a vapour compression system. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates---a problem emphasized in application by the long time constants associated with thermal systems. The proposed method uses time-varying extremum seeking, which has faster and more reliable convergence properties for this application. In particular, we regulate the compressor discharge temperature using a model predictive controller with set-points selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of real-time optimization), and use discrete-time extremum seeking control to drive these set-points to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Further, because the required cooling capacity (and therefore compressor speed) is a function of measured and unmeasured disturbances, the optimal compressor discharge temperature set-point must vary according to these conditions. We show that the energy optimal discharge temperature is tracked with the time-varying extremum seeking algorithm in the presence of disturbances."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.A.Sc."]},{"key":"dc:title","label":"Title","values":["Integration of Extremum Seeking and Model Predictive Control for Discrete Time Systems"]}]}],"canonical_facts":{"dc:contributor.department":["Chemical Engineering"],"dc:contributor.supervisor":["Guay, Martin"],"dc:creator":["Weiss, Walter"],"dc:date":["2014-07-16 13:42:57.628"],"dc:date.accessioned":["2014-07-16T18:46:41Z"],"dc:date.available":["2014-07-16T18:46:41Z"],"dc:date.issued":["2014-07-16"],"dc:description":["Thesis (Master, Chemical Engineering) -- Queen's University, 2014-07-16 13:42:57.628"],"dc:description.abstract":["This thesis considers a time-varying extremum seeking control algorithm that adjusts set-points provided to a model predictive controller for a vapour compression system. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates---a problem emphasized in application by the long time constants associated with thermal systems. The proposed method uses time-varying extremum seeking, which has faster and more reliable convergence properties for this application. In particular, we regulate the compressor discharge temperature using a model predictive controller with set-points selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of real-time optimization), and use discrete-time extremum seeking control to drive these set-points to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Further, because the required cooling capacity (and therefore compressor speed) is a function of measured and unmeasured disturbances, the optimal compressor discharge temperature set-point must vary according to these conditions. We show that the energy optimal discharge temperature is tracked with the time-varying extremum seeking algorithm in the presence of disturbances."],"dc:description.degree":["M.A.Sc."],"dc:identifier.uri":["http://hdl.handle.net/1974/12286"],"dc:language.iso":["eng"],"dc:subject":["MPC","Real Time Optimization","Vapour Compression System","ESC","Compressor Discharge Temperature","VCS","Extremum Seeking Control","Time-Varying Extremum Seeking Control","Discrete-Time","Model Predictive Control"],"dc:title":["Integration of Extremum Seeking and Model Predictive Control for Discrete Time Systems"],"dc:type":["thesis"]},"updated_at":"2026-07-27T20:35:33Z"}