{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/132901"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/132901","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Model-free tracking control of an optical fiber drawing process using deep reinforcement learning","abstract":"A deep reinforcement learning (DRL) approach for tracking control of an optical fiber drawing process is developed and evaluated. The DRL-based control is capable of regulating the fiber diameter to track either steady or varying reference trajectories in the presence of stochasticity and non-linear delayed dynamics of the system. With about 3.5 hours of real-time training, it outperformed other control models such as open-loop control, proportional-integral (PI) control, and quadratic dynamic matrix control (QDMC) in terms of diameter error. It does not require analytical or numerical model of the system dynamics unlike model-based approaches such as linear-quadratic regulator (LQR) or model predictive control (MPC). It can also track reference trajectories that it has never experienced in the training process.¹","abstract_html":"A deep reinforcement learning (DRL) approach for tracking control of an optical fiber drawing process is developed and evaluated. The DRL-based control is capable of regulating the fiber diameter to track either steady or varying reference trajectories in the presence of stochasticity and non-linear delayed dynamics of the system. With about 3.5 hours of real-time training, it outperformed other control models such as open-loop control, proportional-integral (PI) control, and quadratic dynamic matrix control (QDMC) in terms of diameter error. It does not require analytical or numerical model of the system dynamics unlike model-based approaches such as linear-quadratic regulator (LQR) or model predictive control (MPC). It can also track reference trajectories that it has never experienced in the training process.¹","abstract_has_math":false,"creators":["Kim, Sangwoon, (Mechanical engineer) Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Mechanical Engineering","school":null,"contributors":[],"advisors":["Brian W. Anthony."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:08Z","subjects":["Mechanical Engineering."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. 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The DRL-based control is capable of regulating the fiber diameter to track either steady or varying reference trajectories in the presence of stochasticity and non-linear delayed dynamics of the system. With about 3.5 hours of real-time training, it outperformed other control models such as open-loop control, proportional-integral (PI) control, and quadratic dynamic matrix control (QDMC) in terms of diameter error. It does not require analytical or numerical model of the system dynamics unlike model-based approaches such as linear-quadratic regulator (LQR) or model predictive control (MPC). It can also track reference trajectories that it has never experienced in the training process.¹"]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Model-free tracking control of an optical fiber drawing process using deep reinforcement learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Brian W. Anthony."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Mechanical Engineering","MechE"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Mechanical Engineering."],"dc:creator":["Kim, Sangwoon, (Mechanical engineer) Massachusetts Institute of Technology."],"dc:date.accessioned":["2021-10-08T17:10:59Z"],"dc:date.available":["2021-10-08T17:10:59Z"],"dc:date.issued":["2020"],"dc:description":["Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020","Cataloged from the PDF version of thesis.","Includes bibliographical references (pages 73-76)."],"dc:description.abstract":["A deep reinforcement learning (DRL) approach for tracking control of an optical fiber drawing process is developed and evaluated. The DRL-based control is capable of regulating the fiber diameter to track either steady or varying reference trajectories in the presence of stochasticity and non-linear delayed dynamics of the system. With about 3.5 hours of real-time training, it outperformed other control models such as open-loop control, proportional-integral (PI) control, and quadratic dynamic matrix control (QDMC) in terms of diameter error. It does not require analytical or numerical model of the system dynamics unlike model-based approaches such as linear-quadratic regulator (LQR) or model predictive control (MPC). It can also track reference trajectories that it has never experienced in the training process.¹"],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/132901"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. 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