An interleaved, model-supported system identification scheme for the particle accelerator CLIC

The particle accelerator CLIC is a future linear collider, which is developed at CERN. The quality of the particle-beams produced by CLIC is very sensitive to ground motion. The efficiency of the feedback used to counteract ground motion, relies crucially on the quality of the system knowledge. Therefore, we present a system identification scheme to follow changes of accelerator parameters. The algorithm is based on the well-known RLS (recursive least squares) algorithm with exponential forgetting, but adds modifications to improve the learning speed and to address excitation on-strains given by the system. Parallel-running, interleaved RLS algorithms identify parts of the overall system. The different results are combined by using a priori knowledge. Parts that could not be identified directly, are extrapolated with the help of physical models. The modified algorithm can follow system changes with a factor of approx. 30 improved learning speed, compared to the conventional RLS algorithm. It works robustly, in spite of sensor noise and disturbances acting on the excitation signals. The prize that has to be paid is a minimum permanent error of 13% due to model errors. The scheme can easily be adapted to other linear accelerators. Moreover it should be possible to reduce the steady-state error of the identification for other machines, since the main linac of CLIC is an especially difficult system to model and excite.