Feedback Control and Process Tuning
Paper Title Page
MOPHA064 An Off-Momentum Beam Loss Feedback Controller and Graphical User Interface for the LHC 360
 
  • B. Salvachua, D. Alves, G. Azzopardi, S. Jackson, D. Mirarchi, M. Pojer
    CERN, Meyrin, Switzerland
  • G. Valentino
    University of Malta, Information and Communication Technology, Msida, Malta
 
  During LHC operation, a campaign to validate the configuration of the LHC collimation system is conducted every few months. This is performed by means of loss maps, where specific beam losses are deliberately generated with the resulting loss patterns compared to expectations. The LHC collimators have to protect the machine from both betatron and off-momentum losses. In order to validate the off-momentum protection, beam losses are generated by shifting the RF frequency using a low intensity beam. This is a delicate process that, in the past, often led to the beam being dumped due to excessive losses. To avoid this, a feedback system based on the 100 Hz data stream from the LHC Beam Loss system has been implemented. When given a target RF frequency, the feedback system approaches this frequency in steps while monitoring the losses until the selected loss pattern conditions are reached, so avoiding the excessive losses that lead to a beam dump. This paper will describe the LHC off-momentum beam loss feedback system and the results achieved.  
poster icon Poster MOPHA064 [5.005 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA064  
About • paper received ※ 27 September 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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MOPHA069 Automation of the Undulator Middle Plane Alignment Relative to the Electron Beam Position Using the K-Monochromator 375
 
  • S. Karabekyan, S. Abeghyan, W. Freund
    EuXFEL, Schenefeld, Germany
  • L. Fröhlich
    DESY, Hamburg, Germany
 
  The correct K value of an undulator is an important parameter to achieve lasing conditions at free electron lasers. The accuracy of the installation of the undulator in the tunnel is limited by the accuracy of the instruments used in surveying. Moreover, the position of the electron beam also varies depending on its alignment. Another source of misalignment is ground movement and the resulting change in the position of the tunnel. All this can lead to misalignment of the electron beam position relative to the center of the undulator gap up to several hundred microns. That, in turn, will lead to a deviation of the ΔK/K parameter several times higher than the tolerance requirement. An automated method of aligning the middle plane of the undulator, using a K-monochromator, was developed and used at European XFEL. Details of the method are described in this article. The results of the K value measurements are discussed.  
poster icon Poster MOPHA069 [0.780 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA069  
About • paper received ※ 30 September 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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MOPHA114 Achieving Optimal Control of LLRF Control System with Artificial Intelligence 488
 
  • R. Pirayesh, S. Biedron, J.A. Diaz Cruz, M. Martinez-Ramon, S.I. Sosa Guitron
    University of New Mexico, Albuquerque, New Mexico, USA
 
  Artificial Intelligence is a versatile tool to make machines learn the characteristics of a device or a system. In this research, we will be investigating applying deep learning and Gaussian process learning to make a machine learn the optimal settings of a low-level RF (LLRF) control system for particle accelerators. These settings include the multiple controllers’ parameters and the parameters of the LLRF that result in an optimal target function applied to the LLRF. Finding this target function, finding the right machine learning algorithm with the lowest error, and finding the best controller that result in the most optimal target function is the goal of this research.  
poster icon Poster MOPHA114 [0.847 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA114  
About • paper received ※ 09 October 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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MOPHA151 Feasibility of Hardware Acceleration in the LHC Orbit Feedback Controller 584
 
  • L. Grech, D. Alves, S. Jackson, J. Wenninger
    CERN, Meyrin, Switzerland
  • G. Valentino
    University of Malta, Information and Communication Technology, Msida, Malta
 
  Orbit correction in accelerators typically make use of a linear model of the machine, called the Response Matrix (RM), that relates local beam deflections to position changes. The RM is used to obtain a Pseudo-Inverse (PI), which is used in a feedback configuration, where positional errors from the reference orbit as measured by Beam Position Monitors (BPMs) are used to calculate the required change in the current flowing through the Closed Orbit Dipoles (CODs). The calculation of the PIs from the RMs is a crucial part in the LHC’s Orbit Feedback Controller (OFC), however in the present implementation of the OFC this calculation is omitted as it takes too much time to calculate and thus is unsuitable in a real-time system. As a temporary solution the LHC operators pre-calculate the new PIs outside the OFC, and then manually upload them to the OFC in advance. In this paper we aim to find a solution to this computational bottleneck through hardware acceleration in order to act automatically and as quickly as possible to COD and/or BPM failures by re-calculating the PIs within the OFC. These results will eventually be used in the renovation of the OFC for the LHC’s Run 3.  
poster icon Poster MOPHA151 [0.844 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA151  
About • paper received ※ 30 September 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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TUCPL01 Adding Machine Learning to the Analysis and Optimization Toolsets at the Light Source BESSY II 754
 
  • L. Vera Ramirez, T. Mertens, R. Müller, J. Viefhaus
    HZB, Berlin, Germany
  • G. Hartmann
    University of Kassel, Kassel, Germany
 
  The Helmholtz Association has initiated the implementation of the Data Management and Analysis concept across its centers in Germany. At Helmholtz-Zentrum Berlin, both the beamline and the machine (accelerator) groups have started working towards setting up the infrastructure and tools to introduce modern analysis, optimization, automation and AI techniques for improving the performance of the (large scale) user facility and its experimental setups. This paper focuses on our first steps with Machine Learning techniques over the past months at BESSY II as well as organizational topics and collaborations. The presented results correspond to two complementary scenarios. The first one is based on supervised ML models trained with real accelerator data, whose target are real-time predictions for several measurements (lifetime, efficiency, beam loss, …); some of these techniques are also used for additional tasks such as outlier detection or feature importance analysis. The second scenario includes first prototypes towards self-tuning of machine parameters in different optimization cases (injection efficiency, orbit correction, …) with Deep Reinforcement Learning agents.  
slides icon Slides TUCPL01 [8.894 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL01  
About • paper received ※ 27 September 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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TUCPL02 Processing System Design for Implementing a Linear Quadratic Gaussian (LQG) Controller to Optimize the Real-Time Correction of High Wind-Blown Turbulence 761
 
  • M. Kim, S.M. Ammons, B. Hackel, L. Poyneer
    LLNL, Livermore, California, USA
 
  Funding: This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 with document release number LLNL-PROC-792238.
LLNL has developed a low latency, real-time, closed-loop, woofer-tweeter Adaptive Optics Control (AOC) system with a feedback control update rate of greater than 16 kHz. The Low-Latency Adaptive Mirror System (LLAMAS) is based on controller software previously developed for the successful Gemini Planet Imager (GPI) instrument which had an update rate of 1 kHz. By tuning the COTS operating system, tuning and upgrading the processing hardware, and adapting existing software, we have the computing power to implement a Linear-Quadratic-Gaussian (LQG) Controller in real time. The implementation of the LQG leverages hardware optimizations developed for low latency computing and the video game industry, such as fused multiply add accelerators and optimized Fast Fourier Transforms. We used the Intel Math Kernel Library (MKL) to implement the high-order LQG controller with a batch mode execution of 576 6x6 matrix multiplies. We will share our progress, lessons learned and our plans to further optimize performance by tuning high order LQG parameters.
 
slides icon Slides TUCPL02 [2.521 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL02  
About • paper received ※ 03 October 2019       paper accepted ※ 02 October 2020       issue date ※ 30 August 2020  
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TUCPL03 The LMJ Target Diagnostics Integration 767
 
  • S. Tranquille-Marques, P. Prunet
    CEA, LE BARP cedex, France
 
  The French Laser Megajoule (LMJ) is, behind the US NIF, the second largest inertial fusion facility in the World. The main activity of this facility is the acquisition of several physical phenomena as neutron, gamma, X rays produced by the indirect attack of hundreds of high power laser beams on targets through measurement devices called "target diagnostics". More than 30 diagnostics will be installed and driven in a huge and complex integrated computer control system. All this Targets Diagnostics arrived one at a time, each one with its particularity and complexity. The Tango Architecture and Panorama are used for the command control of these equipment. The aim of this paper is first, to introduce how Targets Diagnostics are progressively integrated in the command control. We will then see how Targets Diagnostics managed to cohabit even if they are in different phases of their integration. The paper concludes how Target Diagnostics are configured and computer-driven during all the shot sequence.  
slides icon Slides TUCPL03 [56.870 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL03  
About • paper received ※ 27 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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TUCPL04 A Model-Based Simulator for the LCLS Accelerator 773
 
  • M.L. Gibbs, W.S. Colocho, A. Osman, J. Shtalenkova
    SLAC, Menlo Park, California, USA
 
  The Linac Coherent Light Source (LCLS) at the SLAC National Accelerator Laboratory is currently undergoing a major upgrade. In order to facilitate the development of new software that will be needed to operate the upgraded machine, a simulator has been developed to simulate the LCLS electron beam and the accelerator devices that measure and manipulate it. The simulator is comprised of several small "services" that simulate different types of devices, and provide an EPICS interface identical to the real control system. All of the services communicate with a central beam line model to change accelerator parameters and retrieve information about the simulated beam.  
slides icon Slides TUCPL04 [5.784 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL04  
About • paper received ※ 01 October 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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TUCPL05 ESRF-Double Crystal Monochromator Prototype - Control Concept 776
 
  • M. Brendike, R. Baker, G. Berruyer, L. Ducotté, H. Gonzalez, C. Guilloud, M. Perez
    ESRF, Grenoble, France
 
  The ESRF-Double Crystal Monochromator (ESRF-DCM) has been designed and developed in-house to enable spectroscopy beamlines to exploit the full potential of the ESRF-EBS upgrade. To reach concomitant beam positioning accuracy and beam stability at nanometer scale with a reliable, robust and simple control system, a double cascaded control architecture is implemented. The cascade is comprised of three modes: classic open loop actuation, an optimized open loop mode with error mapping, and closed loop real-time actuation. Speedgoat hardware, programmable from MATLAB/SIMULINK and running at 10 kHz loop frequency is used for the real-time mode. From the EBS startup 2020, the ESRF plans to deploy BLISS – the new BeamLine Instrumentation Support Software control system – for running experiments. An interface between Speedgoat hardware and BLISS has therefore been developed. The DCM and its control architecture have been tested in laboratory conditions. An overview of the concept, implementation and results of the cascaded control architecture and its three modes will be presented  
slides icon Slides TUCPL05 [5.113 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL05  
About • paper received ※ 30 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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TUCPL06 Accelerating Machine Learning for Machine Physics (an AMALEA-project at KIT) 781
 
  • T. Boltz, E. Bründermann, M. Caselle, A. Kopmann, W. Mexner, A.-S. Müller, W. Wang
    KIT, Karlsruhe, Germany
 
  The German Helmholtz Innovation Pool project will explore and provide novel cutting edge Machine Learning techniques to address some of the most urgent challenges in the era of large data harvests in accelerator physics. Progress in virtually all areas of accelerator based physics research relies on recording and analyzing enormous amounts of data. This data is produced by progressively sophisticated fast detectors alongside increasingly precise accelerator diagnostic systems. As KIT contribution to AMALEA it is planned to investigate a design of a fast and adaptive feedback system that reacts to small changes in the charge distribution of the electron bunch and establishes extensive control over the longitudinal beam dynamics. As a promising and well-motivated approach, reinforcement learning methods are considered. In a second step the algorithm will be implemented as a pilot experiment to a novel PCIe FPGA readout electronics card based on Zynq UltraScale+ MultiProcessor System on-Chip (MPSoC).  
slides icon Slides TUCPL06 [5.955 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL06  
About • paper received ※ 27 September 2019       paper accepted ※ 01 November 2019       issue date ※ 30 August 2020  
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TUCPL07 Optimal Control for Rapid Switching of Beam Energies for the ATR Line at BNL 789
 
  • J.P. Edelen, N.M. Cook
    RadiaSoft LLC, Boulder, Colorado, USA
  • K.A. Brown, P.S. Dyer
    BNL, Upton, New York, USA
 
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682.
The Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory will undergo a beam energy scan over the next several years. To execute this scan, the transfer line between the Alternating Gradient Synchrotron (AGS) and RHIC or the so-called the ATR line, must be re-tuned for each energy. Control of the ATR line has four primary constraints: match the beam trajectory into RHIC, match the transverse focusing, match the dispersion, and minimize losses. Some of these can be handled independently, for example orbit matching. However, offsets in the beam can affect the transverse beam optics, thereby coupling the dynamics. Furthermore, the introduction of vertical optics increases the possibilities for coupling between transverse planes, and the desire to make the line spin transparent further complicates matters. During this talk, we will explore three promising avenues for controlling the ATR line, model predictive control (MPC), on-line optimization methods, and hybrid MPC and optimization methods. We will provide an overview of each method, discuss the tradeoffs between these methods, and summarize our conclusions.
 
slides icon Slides TUCPL07 [4.459 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPL07  
About • paper received ※ 08 October 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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WEPHA012 A General Multiple-Input Multiple-Output Feedback Device in Tango for the MAX IV Accelerators 1084
 
  • P.J. Bell, V.H. Hardion, M. Lindberg, V. Martos, M. Sjöström
    MAX IV Laboratory, Lund University, Lund, Sweden
 
  A general multiple-input multiple-output feedback device has been implemented in Tango for various applications in the MAX IV accelerators. The device has a configurable list of sensors and actuators, response matrix inversion, gain and frequency regulation, takes account of the validity of the sensor inputs and may respond to external interlocks. In the storage rings, it performs the slow orbit feedback (SOFB) using the 10 Hz data stream from the Libera Brilliance Plus Beam Position Measurement (BPM) electronics, reading 194 (34) BPMs in the large (small) ring as sensor inputs. The BPM readings are received as Tango events and a corrector-to-BPM response matrix calculation outputs the corrector magnet settings. In the linac, the device is used for the trajectory correction, again with sensor input data sent as Tango events, in this case from the Single Pass BPM electronics. The device is also used for tune feedback in the storage rings, making use of its own polling thread to read the sensors. In the future, a custom SOFB device may be spun off in order to integrate the hardware-based fast orbit feedback, though the general device is also seeing new applications at the beamlines.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA012  
About • paper received ※ 20 September 2019       paper accepted ※ 08 October 2019       issue date ※ 30 August 2020  
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WEPHA021 Free-Electron Laser Optimization with Reinforcement Learning 1122
 
  • N. Bruchon, G. Fenu, F.A. Pellegrino, E. Salvato
    University of Trieste, Trieste, Italy
  • G. Gaio, M. Lonza
    Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
 
  Reinforcement Learning (RL) is one of the most promising techniques in Machine Learning because of its modest computational requirements with respect to other algorithms. RL uses an agent that takes actions within its environment to maximize a reward related to the goal it is designed to achieve. We have recently used RL as a model-free approach to improve the performance of the FERMI Free Electron Laser. A number of machine parameters are adjusted to find the optimum FEL output in terms of intensity and spectral quality. In particular we focus on the problem of the alignment of the seed laser with the electron beam, initially using a simplified model and then applying the developed algorithm on the real machine. This paper reports the results obtained and discusses pros and cons of this approach with plans for future applications.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA021  
About • paper received ※ 30 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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WEPHA124 CERN Accelerators Beam Optimization Algorithm 1379
 
  • E. Piselli, A. Akroh, S. Rothe
    CERN, Geneva, Switzerland
  • K. Blaum, M. Door
    MPI-K, Heidelberg, Germany
  • D. Leimbach
    IKP, Mainz, Germany
 
  In experimental physics, computer algorithms are used to make decisions to perform measurements and different types of operations. To create a useful algorithm, the optimization parameters should be based on real time data. However, parameter optimization is a time consuming task, due to the large search space. In order to cut down the runtime of optimization we propose an algorithm inspired by the numerical method Nelder-Mead. This paper presents details of our method and selected experimental results from high-energy (CERN accelerators) to low-energy (Penning-trap systems) experiments as to demonstrate its efficiency. We also show simulations performed on standard test functions for optimization.  
poster icon Poster WEPHA124 [1.069 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA124  
About • paper received ※ 27 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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