Advancements and challenges of machine learning and deep learning in autonomous control of nuclear reactors

This review paper explores recent advancements in the application of machine learning (ML) and deep learning technologies for autonomous control in nuclear reactors. It covers intelligent diagnosis systems using ML, deep learning algorithms, and hybrid approaches for reactor condition assessment. In the area of intelligent control, traditional methods such as fuzzy control, proportional-integral-derivative (PID) control, and Model Predictive Control (MPC), coupled with neural networks, are discussed, as well as deep reinforcement learning (DRL) for controlling a nuclear reactor. Key challenges are identified, including system integration, cybersecurity, and regulatory adaptation. The review highlights the need for future research on integrating intelligent diagnosis and control systems in real-world reactors, particularly advanced and small modular reactors. It also stresses the importance of considering cybersecurity during the design phase of autonomous control systems and updates of regulatory frameworks to accommodate AI-driven technologies in nuclear power plant operations.