Abstract
As industries embrace the digitalization of Industry 4.0, the abundance of process data creates new opportunities to optimize industrial control systems. Traditional Proportional-Integral-Derivative (PID) controllers often require manual tuning to address changing conditions. This paper introduces an automated, adaptive PID tuning method using historical data and machine learning for a continuously evolving, data-driven approach. The method centers on training a surrogate model using historical process data to replicate real system behavior under various conditions. This enables safe exploration of control strategies without disrupting live operations. An RL (Reinforcement Learning) agent interacts with the surrogate model to learn optimal control policies, dynamically responding to the plant's state, defined by variables like operational conditions and measured disturbances. The agent adjusts PID parameters in real-time, optimizing metrics such as stability, response time, and energy efficiency. After training, the RL agent is deployed online to monitor and adjust PID controllers in response to real-time deviations. The system continuously integrates new data to refine the surrogate model and RL agent, ensuring adaptability to long-term process changes. This continuous learning enhances resilience and scalability, maintaining optimal performance in dynamic environments. By combining data-driven modeling with RL, this method automates PID tuning, maximizing process data utility and aligning with Industry 4.0 principles. It reduces manual oversight while improving efficiency, reliability, and sustainability, addressing the challenges of increasingly complex and data-rich industrial systems.