Machine Learning enhanced deterministic feedback controls in lasers and accelerators.

Lasers and accelerators are inherently complex systems, typically involving multi-input multi-output (MIMO) control problems that demand precise and efficient feedback mechanisms. As these next-generation systems push the boundaries of performance and stability, traditional control techniques often fall short in meeting the stringent requirements for speed and accuracy. This is where Machine Learning (ML) emerges as a transformative tool, enhancing feedback controls by serving as both a real-time error predictor and a robust decision-making agent within the feedback loop. By leveraging data-driven ML models, we can achieve deterministic and rapid control responses that surpass the capabilities of conventional methods.

In this talk, I will present a series of groundbreaking applications where ML has significantly improved feedback control in both laser and accelerator systems. Notably, ML-based control of complex laser combining systems at LBNL has achieved a tenfold improvement in response time compared to traditional methods. Additionally, we have developed and tested streamlined reinforcement learning algorithms for a variety of control scenarios, demonstrating their effectiveness in both accelerator and laser environments. We will also discuss the implementation of ML algorithms on Field Programmable Gate Arrays (FPGAs) for general MIMO control applications. Our work highlights the potential of ML to complement and improve traditional control frameworks, providing valuable tools and methodologies for advancing high-performance laser and accelerator operations.