Model-free tracking control of an optical fiber drawing process using deep reinforcement learning
Model-free tracking control of an optical fiber drawing process using deep reinforcement learning
- Neue Suche nach: Kim, Sangwoon, S.M. Massachusetts Institute of Technology.
- Neue Suche nach: Kim, Sangwoon, S.M. Massachusetts Institute of Technology.
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
Dokumentinformationen
Model-free tracking control of an optical fiber drawing process using deep reinforcement learning
2020-01-01
1263359134
Hochschulschrift
Elektronische Ressource
Englisch
| DDC: | 629 |