Adaptive Control Meets Deep Learning: EXTREMUM SEEKING IN LATENT SPACE WITH PARTICLE ACCELERATOR APPLICATIONS

Generative deep learning has recently emerged as a transformational technology for a wide range of tasks, including image generation from user-defined captions, prediction of 3D protein structures directly from DNA sequences, and large language models (LLMs) that can write custom computer code based on user prompts. One major challenge faced by machine learning (ML)- and artificial intelligence (AI)-based tools is that of time-varying systems or systems with distribution shift. ML typically relies on brute-force retraining to readjust learned models when systems change. Extremum seeking (ES) is a model-independent adaptive feedback technique that can be used to stabilize unknown and open-loop unstable time-varying dynamic systems and to optimize their analytically unknown time-varying output functions. ES is model independent and robust to both noise and time variations. This article provides a brief review of several generative deep learning techniques and how they have been combined with ES so that while the generative models act as highly detailed virtual diagnostics of otherwise inaccessible system states, incorporating adaptive feedback within their latent embeddings increases their robustness for use with uncertain and time-varying systems. The approach is demonstrated with applications for time-varying charged particle beams in particle accelerators.