Model-free tracking control of an optical fiber drawing process using deep reinforcement learning

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

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Kim, Sangwoon, (Mechanical engineer) Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Brian W. Anthony.

• Mechanical Engineering.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/132901

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132901