Iterative Learning Predictive Control Method Based on Reinforcement Learning for Rubber Mixing Batch Process

As a typical batch production process, the rubber mixing process involves complex dynamics and significant fluctuations in results. Since the mixing temperature is key to product quality, this paper proposes an iterative learning model predictive control (ILMPC) strategy that integrates reinforcement learning (RL) for the temperature control model, utilizing the principle of reaction heat balance. The Iterative Learning Control (ILC) enhances performance across repetitive batches. Simultaneously, Model Predictive Control (MPC), leveraging its predictive model, compensates for external disturbances, ensuring primary tracking performance. To address the model accuracy dependence of ILMPC, RL is integrated to enhance the system’s robustness against non-repetitive disturbances and uncertainties through data-driven adaptive learning, optimizing overall performance. Finally, simulation experiments on a rubber mixing temperature control process demonstrate the superior control performance of the proposed method.