Shihao Li, Jiachen Li, Christopher Martin, Zijun Chen, Dongmei Chen, Wei Li
{"title":"Curriculum-Based Soft Actor-Critic for Multi-Section R2R Tension Control","authors":"Shihao Li, Jiachen Li, Christopher Martin, Zijun Chen, Dongmei Chen, Wei Li","doi":"10.1016/j.mfglet.2026.07.018","DOIUrl":null,"url":null,"abstract":"<div><div>Precise tension control in roll-to-roll (R2R) manufacturing systems remains challenging under varying operating conditions and process uncertainties. While they perform well for systems with accurate models and small disturbances, model-based controllers face limitations when operating conditions deviate from design points or when process uncertainties are large. This paper presents a curriculum-based Soft Actor-Critic (SAC) approach as an alternative to R2R control under uncertainty. The method learns control policies through progressive exposure to various operating conditions without requiring system-specific parameter tuning. We train SAC with a three-phase curriculum with gradual expansion of reference trajectory diversity, from a narrow ranges (27N–33N) to full operational envelope (20N–40N), enabling the policy to generalize across the complete operational range. Evaluation on a three-section R2R system shows that the learned policy maintains tight tension tracking under nominal conditions and handles large disturbances (20N<span><math><mrow><mo>→</mo></mrow></math></span>40N step changes) well with a single control policy. The proposed approach offers an alternative to traditional model-based methods for applications where operating conditions vary and process uncertainties are significant, typical for R2R manufacturing of flexible devices.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"49 ","pages":"Pages 86-97"},"PeriodicalIF":2.8000,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Manufacturing Letters","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213846326000696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/10/5 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Precise tension control in roll-to-roll (R2R) manufacturing systems remains challenging under varying operating conditions and process uncertainties. While they perform well for systems with accurate models and small disturbances, model-based controllers face limitations when operating conditions deviate from design points or when process uncertainties are large. This paper presents a curriculum-based Soft Actor-Critic (SAC) approach as an alternative to R2R control under uncertainty. The method learns control policies through progressive exposure to various operating conditions without requiring system-specific parameter tuning. We train SAC with a three-phase curriculum with gradual expansion of reference trajectory diversity, from a narrow ranges (27N–33N) to full operational envelope (20N–40N), enabling the policy to generalize across the complete operational range. Evaluation on a three-section R2R system shows that the learned policy maintains tight tension tracking under nominal conditions and handles large disturbances (20N40N step changes) well with a single control policy. The proposed approach offers an alternative to traditional model-based methods for applications where operating conditions vary and process uncertainties are significant, typical for R2R manufacturing of flexible devices.