Superconducting Radio-Frequency (SRF) cavities operating in continuous wave (CW) mode and with low beam loading are devices characterized by a high loaded quality factor, in the order of 10^7, and narrow bandwidth, in the order of 10 Hz. The Low Level RF (LLRF) and resonance control systems of SRF cavities become a fundamental component of the entire system operation and in general has very tight stability requirements on the amplitude, phase and resonance frequency of the cavity. Microphonics plays an important role in cavity detuning, which results in more power needed to achieve the desired gradient. Active control approaches to reduce detuning in SRF cavities using piezoelectric actuators have shown promising results. Furthermore, Machine Learning (ML) techniques have also shown important capabilities to improve existing PID controllers. Specifically, Neural Networks (NN) can be used to find optimal PID gains and improve performance of traditional control systems. In this research, we develop new approaches to improve existing LLRF and resonance control systems with ML as a tool to find the optimal gains. We investigate these approaches using the LLRF control system intended for LCLS-II.

Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

/pdf/neural-networks-for-modeling-and-control-of-particle-23jaee3low.pdf

Neural Networks for Modeling and Control of Particle Accelerators

The Radio Frequency (RF) system at the Heavy Ion Research Facility in Lanzhou of the Institute of Modern Physics was upgraded to a digital low-level radio frequency system. The key part is the phase and amplitude negative feedback system based on Digital Signal Processing (DSP) and a Field Programmable Gate Array (FPGA). This paper mainly introduces the design of the phase stabilization loop, including the principle designs for phase stabilization, loop filter design, parameter calculation, and hardware. In order to improve the performance and stability of the system, the loop filter was designed by means of zero-pole cancellation. In this paper, the derivation of the “zero pole cancellation method” is introduced in detail. The implementation structure of the loop filter in the FPGA is an ingenious design that can be applied to RF systems with different cavity loaded quality factor ( Q L ) values. For different RF systems, the parameters of proportional integral (PI) algorithm are fixed, and only one parameter Q L needs to be set. An economical compensation method for the temperature drift in the pick-up cables was designed to improve the control accuracy of the system. The phase control system was tested in long-term stability experiments, and the results show that the system has excellent phase stability over the long term, as well as parasitic phase modulation suppression.

Phase stabilization design for radio-frequency system of the IMP HIRFL-SSC

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

/pdf/neural-networks-for-modeling-and-control-of-particle-qdnl2m8hfi.pdf

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.

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

The current LANSCE LLRF system is an analog PI Feedback control system which achieves the amplitude and phase error within 1% and 1 degree. The feedback system receives the cavity amplitude and phase and the crosstalk between the amplitude and the phase paths is significant. We propose an In-phase (I) and Quadrature (Q) based feedback control system which easily decouples the crosstalk of I and Q channels. A gain scheduled PI feedback controller with optimally generated set point trajectory reduces the transient peak of the feedback controller and hence reduce the fatigue of the RF amplifier chain. An additional feature of the controller is the Neural network based self-tuning PI feedback, where the neural network tunes the feedback gains to minimize the errors in the least squares sense. The proposed control system is implemented with Altera Stratix II FPGA. The control system is modeled with DSP Builder and automatically generates HDL. Altera SOPC Builder is used for the hardware integration of the DSP Builder model, memories, peripherals, and 32 bit NIOS II embedded processor. NIOS II processor equipped with real time operating system communicates with the host computer via Ethernet, uploads data, computes parameters, and downloads parameters. The proposed control system is tested with the low power test-stand for the robustness of the algorithm.

Gain scheduled neural network tuned pi feedback control system for the lansce accelerator

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Gain scheduled neural network tuned pi feedback control system for the lansce accelerator