Structured Representation Learning for Behavior Cloning: How can we learn to safely control a nuclear power plant?
This study addresses the challenge of ensuring safety when embedding industrial control models into deployed systems, particularly regarding safety constraints in pressurized water reactor (PWR) load-following operations. To this end, a physics-decomposition-based structured representation learning method is proposed. This approach constructs a multi-timescale separation embedding architecture that maps variables across different timescales into independent latent spaces to emulate expert policies. It further enables hybrid deployment by integrating behavioral cloning with nonlinear model predictive control (NMPC). Experimental results demonstrate that the proposed method significantly improves the accuracy and feasibility of long-horizon trajectories, achieving fully feasible solutions with near-optimal costs while reducing computation time by approximately 15% compared to the expert controller.