“Reinforcement Learning” Fuels the Rise of Adaptive Controllers (Download)
Industrial process control has traditionally relied on fixed-parameter controllers such as proportional integral derivative (PID) and model-based approaches like model predictive control (MPC). While these methods are well understood and robust, they often struggle to maintain optimal performance in nonlinear, time-varying, or poorly modeled systems.
One promising technique for adaptive and self-tuning control is reinforcement learning (RL), which enables controllers to learn optimal policies directly from interaction with the process. RL can be integrated into industrial control systems through hybrid architectures, highlighting safety and real-time considerations, and by applying appropriate hardware implementation.