{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117840"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117840","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven methods for design of model predictive controllers","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Edwards, William"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hauser, Kris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Robotics","Model Predictive Control","Automatic Tuning"],"languages":["en","eng"],"rights":["Copyright 2022 William Edwards"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117840","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hauser, Kris"]},{"key":"dc:creator","label":"Author","values":["Edwards, William"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-07"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robotics","Model Predictive Control","Automatic Tuning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 William Edwards"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117840"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, William Edwards, accepted the attached license on 2022-12-06 at 22:07.","The student, William Edwards, submitted this Thesis for approval on 2022-12-06 at 22:29.","This Thesis was approved for publication on 2022-12-07 at 11:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18765 on 2023-04-12 at 07:39:14","Model predictive control (MPC) is a powerful feedback technique that is often used in data-driven robotics. The performance of data-driven MPC depends on the accuracy of the model, which often requires careful tuning. Furthermore, specifying the task with an objective function and synthesizing a feedback policy are not straightforward and typically lead to suboptimal solutions driven by trial and error. In this work, we seek to address these challenges by investigating data-driven methods for system identification, task specification, and control synthesis of unknown dynamical systems. First, we conduct a case study on the design of a data-driven MPC for performing automatic needle insertion in deep anterior lamellar keratoplasty, a challenging ophthalmic microsurgery task. We propose a data-driven method for controller synthesis and selection and demonstrate that the synthesized controller outperforms a state-of-the-art baseline in ex vivo physical experiments. Next, we present AutoMPC, an open-source Python package for automatic synthesis of data-driven MPC. We demonstrate the AutoMPC outperforms a state-of-the-art offline reinforcement learning algorithm on several standard control benchmarks. We further demonstrate that AutoMPC outperforms standard control baselines in physical experiments on an underwater soft robot."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven methods for design of model predictive controllers"]}]}],"canonical_facts":{"dc:contributor":["Hauser, Kris"],"dc:creator":["Edwards, William"],"dc:date":["2022-12","2022-12-07"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, William Edwards, accepted the attached license on 2022-12-06 at 22:07.","The student, William Edwards, submitted this Thesis for approval on 2022-12-06 at 22:29.","This Thesis was approved for publication on 2022-12-07 at 11:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18765 on 2023-04-12 at 07:39:14","Model predictive control (MPC) is a powerful feedback technique that is often used in data-driven robotics. The performance of data-driven MPC depends on the accuracy of the model, which often requires careful tuning. Furthermore, specifying the task with an objective function and synthesizing a feedback policy are not straightforward and typically lead to suboptimal solutions driven by trial and error. In this work, we seek to address these challenges by investigating data-driven methods for system identification, task specification, and control synthesis of unknown dynamical systems. First, we conduct a case study on the design of a data-driven MPC for performing automatic needle insertion in deep anterior lamellar keratoplasty, a challenging ophthalmic microsurgery task. We propose a data-driven method for controller synthesis and selection and demonstrate that the synthesized controller outperforms a state-of-the-art baseline in ex vivo physical experiments. Next, we present AutoMPC, an open-source Python package for automatic synthesis of data-driven MPC. We demonstrate the AutoMPC outperforms a state-of-the-art offline reinforcement learning algorithm on several standard control benchmarks. We further demonstrate that AutoMPC outperforms standard control baselines in physical experiments on an underwater soft robot."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117840"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 William Edwards"],"dc:subject":["Robotics","Model Predictive Control","Automatic Tuning"],"dc:title":["Data-driven methods for design of model predictive controllers"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}