Speaker
Description
Feedback control is an essential component for the successful operation of particle accelerators. However, achieving the desired closed-loop performance requires precise model knowledge, which is difficult to obtain in complex accelerator systems. For this reason, we present an application of a combined optimization approach that estimates the response matrix online while optimizing the chosen performance measure, eliminating the need for first-principles modelling or a priori identification experiments. Specifically, a Kalman filter is employed to construct a linearization of the system response around its operating point from noisy input-output measurements, iteratively improving the available knowledge about the system. In parallel, this knowledge is exploited by a feedback optimizer, which is incrementally driving the system to its optimal operating point while maintaining safety of operation as formulated by constraints on inputs and outputs. As a consequence of the continuous online response estimation and in contrast to other modelling approaches, the scheme is able to instantaneously react to changes and drifts in the system behaviour. This is demonstrated on the orbit feedback of the main electron beam dump line of the European XFEL.
