A Reinforcement-Learning-Based Fuzzy PID Control Strategy for the Unwinding Tension System of a Lithium-Ion Battery Coating Machine

Abstract

Stable tension is critical for the coating quality of lithium-ion battery electrodes. As the origin of tension control, the unwinding system’s control accuracy governs the stability of downstream processes and the final yield. To achieve the required precision, we propose a control strategy that combines reinforcement learning and fuzzy PID. We first derived a nonlinear time-varying dynamic model of the unwinding tension system based on the unwinding mechanism. Leveraging this model, we then designed a reinforcement-learning-based fuzzy PID controller. Finally, we validated the performance of the proposed control strategy through simulations and experiments. Simulations and experiments confirm that, under varying coil radius, the reinforcement-learning-based fuzzy PID controller outperforms both the conventional PID and fuzzy PID controllers in robustness, effectively accommodating the effects of time-varying tension system parameters. Moreover, this method substantially improves the dynamic performance of the unwinding system, with pronounced overshoot suppression, and demonstrates superior robustness and disturbance rejection capabilities.