2026-09-18T22:52:41Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1329012025-10-30T15:50:02Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Brian W. Anthony.
Kim, Sangwoon,
(Mechanical engineer)
Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2021-10-08T17:10:59Z
2021-10-08T17:10:59Z
2020
2020
https://hdl.handle.net/1721.1/132901
1263359134
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).
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
by Sangwoon Kim.
S.M.
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
76 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Model-free tracking control of an optical fiber drawing process using deep reinforcement learning
Thesis
application/pdf
2021-10-08T17:10:59Z
Publication
Master
MechE
unknown
unknown
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<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>
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