Exploring Strategies for Data-Driven Motion Control of Multi-Modal Actuators in Synchrotron Experimentation

Challenged by the tremendous increase in photon flux provided at next-generation synchrotron facilities, instrumentation must keep up with significantly increased acquisition speeds by developing fundamentally new control strategies. This project focuses specifically on novel approaches to motion control in scientific synchrotron instrumentation by exploring new pathways opened up by combining groundbreaking advancements in electronics and data-driven approaches in mechatronics. It addresses practical experimental challenges that occur in numerous science-driven use-cases by studying possibilities of modern embedded system engineering (including embedded machine learning (ML) and artificial intelligence (AI) in general) from a computer and data science point of view. Specific problems to be addressed are the dynamics of multi-modal actuators commonly found at experimental end stations of synchrotron beamlines and continuous (non-stop) motions with simultaneous data acquisition. AI-based real-time feedback into ongoing multi-axes, trajectory-based motion control will be employed for in-depth studies of the dynamics of heterogenous electromechanical systems. The latter specifically addresses the multi-modal actuators mentioned above, combining nanometric piezo positioners to position samples of a few grams and undulator gap control, moving tons of equipment in the accelerator tunnel. Using DESY’s leading position in the development and design of MicroTCA components, a modular electronics standard that enables high-performance, reliable data processing, and control operations at large-scale scientific research facilities and in industry is to be established. We envision that this project supports the PETRA VI mission of providing world-class instrumentation.