Improving the PID Controllers of Roll-to-Roll Processes using Reinforcement Learning - Lima, Luís Eduardo Fernandes Costa; Pfitscher, Ricardo José (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
Approximately 90% of control loops in industrial systems utilize Proportional Integral Derivative (PID) controllers, which are essential for ensuring product quality in roll-to-roll (R2R) manufacturing processes. These processes, which involve the continuous handling of materials on rolls, require precise control, especially of substrate tension, as it is important to control because this variable is directly connected to the quality of the final product. Traditional tuning of PID parameters can be complex, as it requires the mathematical formulation of all the dynamics of the process, making it laborious to define using the traditional approach. However, advances in computational techniques have facilitated the development of automated tuning methods for PID controllers. This article investigates the application of reinforcement learning, an artificial intelligence technique, to optimize the tuning of PID controllers. The proposed methodology occurs in two stages: first, we create a simulation environment to prevent damage to real R2R machines; then, we use the CARLA reinforcement learning algorithm to adjust the PID parameters. The results indicate a 65.1% reduction in costs compared to traditional empirical tuning methods, demonstrating a significant improvement in process efficiency.