Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning
| Autor: | Sangwoon Kim, David Donghyun Kim, Brian W. Anthony |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mean squared error
Process (computing) PID controller Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Computer Science Applications Step response Control and Systems Engineering Control theory Control system Trajectory FOS: Electrical engineering electronic engineering information engineering Reinforcement learning Electrical and Electronic Engineering Mathematics |
| Popis: | This paper presents a model-free deep reinforcement learning (DRL) approach for controlling a fiber drawing system. The custom DRL-based control system predictively regulates fiber diameter and produces a fiber with a desired, constant or non-constant, diameter trajectory, i.e. diameter variation along the fiber length. Physical models of the system are not used. The system was trained and tested on a compact fiber drawing system, which has non-linear delayed dynamics and stochastic behaviors. For a reference trajectory with random step changes, after 1 hour of training, the DRL controller showed the same root mean squared error (RMSE) as an optimized PI controller; after 3 hours of training, it achieved the performance of a quadratic dynamic matrix controller (QDMC). While the PI feedback controller showed 3.5 seconds of time lag in a step response, the DRL controller showed less than a second of time lag. Controller performance tests on trajectories not used in the training process are conducted; for a sine sweep reference trajectory, the DRL controller maintained an RMSE under 40 um up to a frequency of 45 mHz, compared to 25 mHz for QDMC. 12 pages, 8 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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