Controlling Aluminum Strip Thickness by Clustered Reinforcement Learning With Real-World Dataset

• Published in: IEEE transactions on industrial informatics 2024-08, Vol.20 (8), p.9928-9938
• Main Authors: Xiao, Ziqi, He, Zhili, Liang, Huanghuang, Hu, Chuang, Cheng, Dazhao
• Format: Article
• Language: English
• Subjects: Aluminum; Aluminum strip cold rolling; Automatic control; clustered approach; Cold rolling; Control data (computers); Control methods; Costs; Data models; data-driven; Datasets; Deep reinforcement learning; deep reinforcement learning (DRL); Markov processes; Metal sheets; Multivariable control; Optimization; Process control; Process controls; Production; real-world dataset; Strip; Thickness; Thickness control
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2024.3390625

Consistent thickness in aluminum strips stands as a pivotal indicator of aluminum sheet product quality. Conventional automatic gauge control systems are complex, multivariable, and strongly coupled. However, the rolling process faces uncertainties, preventing the establishment of precise mathematical models. To tackle this, we propose an aluminum strip cold rolling thickness control method grounded in offline reinforcement learning. To facilitate the learning of better control policies, we construct a dataset of aluminum strip cold rolling process control data derived from real-world historical records and expertise for offline policy training, which is named dataset for aluminum strip cold rolling and comprises 8 373 540 Markov decision process tuples. We employ a clustered approach to handle time-varying production conditions. A constrained filtering scheme is introduced to eliminate problematic data after a data-driven ensemble rolling model is established. Evaluation and case study demonstrate that our method effectively reduces aluminum strip thickness deviations without requiring prior knowledge, thus improving control performance.