Speaker
Description
In advanced accelerator facilities like the heavy-ion synchrotron SIS18 at GSI in Darmstadt, ensuring stable and efficient multi-turn injection is crucial for achieving high-intensity beams. However, conventional control methods often lack the adaptability needed to handle rapidly changing beam dynamics, leading to suboptimal performance. To address this limitation, a data-driven Gaussian Process Model Predictive Control (GP-MPC) framework is employed, leveraging real-time updates to capture and predict complex injection behavior more accurately. We also systematically analyze the controller’s behavior under variations in the incoming beam intensity and emittance, assessing its robustness against such perturbations.
Our simulation results demonstrate that GP-MPC can consistently manage multi-turn injection under realistic fluctuations of the incoming beam. By providing enhanced predictive accuracy through sample-efficient data-driven modeling, this work lays the groundwork for more robust and automated control strategies in such accelerator operations.
