Model-free tracking control of an optical fiber drawing process using deep reinforcement learning
Name
1263359134-MIT.pdf
Size
9.67 MB
Format
Adobe PDF
Checksum (MD5)
13fa1599711e25afa4bac9560a2590a1
Author(s)
Kim, Sangwoon,
(Mechanical engineer)
Massachusetts Institute of Technology.
Advisor(s)
Brian W. Anthony.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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
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).
Subjects
Mechanical Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
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.
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