High-Precision Temperature Control in Industrial Evaporation Heaters via Hybrid Reinforcement Learning Approach

Abstract

Precise temperature regulation for the evaporation process in display manufacturing remains a significant challenge due to the nonlinearity and complexity of dynamics, safety constraints, and slow thermal responses. The conventional proportional-integral-derivative (PID) control approach has been widely used due to its simplicity; however, it often fails to maintain stability and accuracy under such nonlinear and time-varying conditions. To overcome these limitations, this paper proposes a reinforcement learning–based temperature control framework tailored for the evaporation process. A data-driven heating simulator is first developed to provide a safe and efficient virtual environment for reinforcement learning (RL) training, eliminating the need for costly and risky high-temperature experiments. A hybrid-mode RL architecture is then introduced, consisting of two agents: one for directly controlling power in the early phase and another for RL-based PID gain adjustment in the later phase. Experimental results from both the simulator and the actual heating system demonstrate that the proposed framework achieves stable temperature control. Moreover, our proposed approach significantly outperforms conventional PID control by reducing delay time by 6.90%, maximum overshoot by 94.82%, and settling time by 36.47%. For nonlinear high-temperature processes, the proposed method successfully closes the gap between RL-based control and conventional PID.