Optimisation of linear accelerator performance for single-pass free-electron laser operation

This project is part of a collaboration betweenMonash University, the Aus-
tralian Synchrotron (AS), the new FERMI@Elettra project, and the Linac Co-
herent Light Source (LCLS) at the SLAC National Accelerator Laboratory.
The thesis investigates the use of Artificial Intelligence systems and their
applicability to machine optimisation and control for linear accelerators,
and in particular Free Electron Lasers (FEL). This research is motivated by
the need to develop adaptive systems for beam tuning and stabilisation,
in order to meet the increasingly stringent requirements of new generation
light sources.

The thesis begins with the simulation of a feedback system for the
FERMI@Elettra Linac, based on the Proportional - Integral - Differential
(PID) scheme developed for the LCLS. To facilitate these simulations, aMat-
lab Graphical User Interface was built in order to incorporate various con-
trol parameters, including possible actuators, observable variables and per-
turbations. These simulations highlight the difficulties encountered with
PID control, which necessitates a more sophisticated approach to the con-
trol of the linear accelerator.

To address the intrinsic limitations of conventional PID control, a com-
bination of a feedforward - feedback system was investigated. The feedfor-
ward component uses a neural network (NNet), which provides a prediction
of the perturbation in the electron beam parameters based on fluctuations
in the voltage and phase of the klystrons. The feedback component con-
sists of a simple PID algorithm, used to compensate for potential inaccura-
cies of the feedforward correction. Experimental results performed at the AS show the viability of the system, by demonstrating the successful con-
trol of the energy at the end of the Linac. The experiments carried out at the
LCLS show the applicability of the system to a multi-variable system with
the simultaneous control of the energy and bunch length. Although these
results demonstrate the ability of the NNet predictor to compensate for the
deficiencies of the PID algorithm, further refinements of the technique are
required to produce a system that can adapt to changes in machine param-
eters and jitter conditions.

To correct the remaining deficiencies of the combined feedforward -
feedback control system, we consider an intelligent system capable of self-
learning. In this scenario the control system is treated from the perspective
of an optimisation problem and a novel optimisation tool is designed, us-
ing state of the art developments in video games. The key principle is to
exploit similarities between the navigation of a game agent in a battlefield
and the navigation of an "optimisation agent" in a search space. This novel
approach was tested using simulations and experiments conducted at the
AS and on the FERMI@Elettra Linac. The experiments conducted at the
AS showed the system’s ability to simultaneously optimise the beam energy
spread and transmission (i.e. the percentage of particles transmitted from
the start to the end of the accelerator). We have demonstrated an increase
in the transmission from 90% to 97% and a decrease in the energy spread of
the beam from 1.04% to 0.91%. Control experiments performed at the new
FERMI@Elettra FEL are also reported, which highlight the adaptability of
the system for beam-based control, in the case where a static perturbation
is applied to the klystron phase. These results show that NNets can be suc-
cessfully exploited to build an optimisation tool that can self-learn from its
interaction with the machine and operate a simple control task. Our results
indicate that this optimisation tool can be used for the stabilisation of the
electron beam parameters when it is subject to time dependent perturba-
tions.
The thesis concludes with suggestions for future work. This includes
the adaptation of the optimisation tool to N-dimensional search spaces,
and the development of a novel control system which merges the NNet pre-
dictor with the structure of the NNet used for optimisation. By combining
these two structures, it is anticipated that the resulting NNet will have the
ability to correct time dependent perturbations, while self-adapting its re-
sponse when machine parameters and jitter conditions change.