{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dynamic-control-of-a-fiber-manufacturing","title":"Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning","arxiv_id":"1911.10286","date":"2019-11-23","proceeding":null,"authors":[],"abstract":"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.","url_abs":"https://arxiv.org/abs/1911.10286v2","url_pdf":"https://arxiv.org/pdf/1911.10286v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dynamic-control-of-a-fiber-manufacturing","repo_url":"https://github.com/sangwkim/RL_manufacturing","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}