Experimental Safe Extremum Seeking for Accelerators

Williams, Alan Brian; Scheinker, Alexander; Huang, En-Chuan; Taylor, Charles Edward; Krstic, Miroslav

doi:10.1109/tcst.2024.3377828

Title: Experimental Safe Extremum Seeking for Accelerators

Journal Article · 22 March 2024 · IEEE Transactions on Control Systems Technology

DOI: https://doi.org/10.1109/tcst.2024.3377828 · OSTI ID:2332792

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Univ. of San Diego, San Diego, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Univ. of San Diego, San Diego, CA (United States)

Here, we demonstrate the recent designs of safe extremum seeking (Safe ES) on the 1-km-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES is a modification of extremum seeking (ES) which, in addition to minimizing an analytically unknown cost, also employs a safety filter based on an analytically unknown control barrier function (CBF) safety metric. Tuning is necessitated by accelerators being large complex systems, with many drifting parameters due to thermal effects and degradation. At the same time, safe operation (the maintenance of state constraints) is crucial, as damage brings astronomical costs, both financially and in operation downtime. Our measured (but analytically unknown) safety metric is the beam current. We perform multivariable Safe ES on three accelerator applications, in which we adapt 4, 6, and 3 magnet strength parameters, respectively. Two of the three applications are for validated simulation models of beamlines at LANSCE: the first for the proton radiography (pRad) beamline of 800-MeV protons for spot size tuning; the second on a high-performance code, HPSim, for tuning the low-energy beam transport (LEBT) region that contains a beam of 750-keV protons. The third is an experimental tuning of the steering magnets in the LEBT at LANSCE.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2332792

Report Number(s):: LA-UR--23-29586

Journal Information:: IEEE Transactions on Control Systems Technology, Journal Name: IEEE Transactions on Control Systems Technology Journal Issue: 5 Vol. 32; ISSN 1063-6536

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (40)

Real-Time Optimization by Extremum-Seeking Control Ariyur, Kartik B.; Krstić, Miroslav https://doi.org/10.1002/0471669784	book	September 2003
A constrained extremum-seeking control approach: A CONSTRAINED EXTREMUM-SEEKING CONTROL APPROACH Guay, Martin; Moshksar, Ehsan; Dochain, Denis International Journal of Robust and Nonlinear Control, Vol. 25, Issue 16 https://doi.org/10.1002/rnc.3254	journal	October 2014
Stability of extremum seeking feedback for general nonlinear dynamic systems Krstić, Miroslav; Wang, Hsin-Hsiung Automatica, Vol. 36, Issue 4 https://doi.org/10.1016/S0005-1098(99)00183-1	journal	April 2000
An 800-MeV proton radiography facility for dynamic experiments King, N. S. P.; Ables, E.; Adams, Ken Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 424, Issue 1 https://doi.org/10.1016/S0168-9002(98)01241-8	journal	November 1999
On non-local stability properties of extremum seeking control Tan, Ying; Nešić, Dragan; Mareels, Iven Automatica, Vol. 42, Issue 6 https://doi.org/10.1016/j.automatica.2006.01.014	journal	June 2006
Shahshahani gradient-like extremum seeking Poveda, Jorge I.; Quijano, Nicanor Automatica, Vol. 58 https://doi.org/10.1016/j.automatica.2015.05.002	journal	August 2015
Bounded extremum seeking with discontinuous dithers Scheinker, Alexander; Scheinker, David Automatica, Vol. 69 https://doi.org/10.1016/j.automatica.2016.02.023	journal	July 2016
Constrained extremum seeking of a MIMO dynamic system Liao, Chwen-Kai; Manzie, Chris; Chapman, Airlie Automatica, Vol. 108 https://doi.org/10.1016/j.automatica.2019.108496	journal	October 2019
An extremum seeking-based approach for Nash equilibrium seeking in N-cluster noncooperative games Ye, Maojiao; Hu, Guoqiang; Xu, Shengyuan Automatica, Vol. 114 https://doi.org/10.1016/j.automatica.2020.108815	journal	April 2020
Constrained Extremum Seeking: a Modified-Barrier Function Approach Labar, Christophe; Garone, Emanuele; Kinnaert, Michel IFAC-PapersOnLine, Vol. 52, Issue 16 https://doi.org/10.1016/j.ifacol.2019.12.043	journal	January 2019
Beam matching adaptive control via extremum seeking Schuster, Eugenio; Xu, Chao; Torres, Nicholas Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 581, Issue 3 https://doi.org/10.1016/j.nima.2007.07.154	journal	November 2007
Ultra low vertical emittance at SLS through systematic and random optimization Aiba, M.; Böge, M.; Milas, N. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 694 https://doi.org/10.1016/j.nima.2012.08.012	journal	December 2012
Machine learning for beam dynamics studies at the CERN Large Hadron Collider Arpaia, P.; Azzopardi, G.; Blanc, F. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 985 https://doi.org/10.1016/j.nima.2020.164652	journal	January 2021
Extremum seeking with bounded update rates Scheinker, Alexander; Krstić, Miroslav Systems & Control Letters, Vol. 63 https://doi.org/10.1016/j.sysconle.2013.10.004	journal	January 2014
Online multi-objective particle accelerator optimization of the AWAKE electron beam line for simultaneous emittance and orbit control Scheinker, Alexander; Hirlaender, Simon; Velotti, Francesco Maria AIP Advances, Vol. 10, Issue 5 https://doi.org/10.1063/5.0003423	journal	May 2020
Comparison of profile measurements and TRANSPORT beam envelope predictions along the 80-m LANSCE pRad beamline Roy, Prabir K.; Taylor, Charles E.; Pillai, Chandra Journal of Physics: Conference Series, Vol. 1067 https://doi.org/10.1088/1742-6596/1067/6/062002	journal	September 2018
Adaptive machine learning for time-varying systems: low dimensional latent space tuning Scheinker, A. Journal of Instrumentation, Vol. 16, Issue 10 https://doi.org/10.1088/1748-0221/16/10/P10008	journal	October 2021
Robust simplex algorithm for online optimization Huang, Xiaobiao Physical Review Accelerators and Beams, Vol. 21, Issue 10 https://doi.org/10.1103/PhysRevAccelBeams.21.104601	journal	October 2018
Model-independent tuning for maximizing free electron laser pulse energy Scheinker, Alexander; Bohler, Dorian; Tomin, Sergey Physical Review Accelerators and Beams, Vol. 22, Issue 8 https://doi.org/10.1103/PhysRevAccelBeams.22.082802	journal	August 2019
Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems Edelen, Auralee; Neveu, Nicole; Frey, Matthias Physical Review Accelerators and Beams, Vol. 23, Issue 4 https://doi.org/10.1103/PhysRevAccelBeams.23.044601	journal	April 2020
Sample-efficient reinforcement learning for CERN accelerator control Kain, Verena; Hirlander, Simon; Goddard, Brennan Physical Review Accelerators and Beams, Vol. 23, Issue 12 https://doi.org/10.1103/PhysRevAccelBeams.23.124801	journal	December 2020
Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster St. John, Jason; Herwig, Christian; Kafkes, Diana Physical Review Accelerators and Beams, Vol. 24, Issue 10 https://doi.org/10.1103/PhysRevAccelBeams.24.104601	journal	October 2021
Tuning particle accelerators with safety constraints using Bayesian optimization Kirschner, Johannes; Mutný, Mojmir; Krause, Andreas Physical Review Accelerators and Beams, Vol. 25, Issue 6 https://doi.org/10.1103/PhysRevAccelBeams.25.062802	journal	June 2022
Predicting the transverse emittance of space charge dominated beams using the phase advance scan technique and a fully connected neural network Mayet, F.; Hachmann, M.; Floettmann, K. Physical Review Accelerators and Beams, Vol. 25, Issue 9 https://doi.org/10.1103/PhysRevAccelBeams.25.094601	journal	September 2022
Uncertainty aware anomaly detection to predict errant beam pulses in the Oak Ridge Spallation Neutron Source accelerator Blokland, Willem; Rajput, Kishansingh; Schram, Malachi Physical Review Accelerators and Beams, Vol. 25, Issue 12 https://doi.org/10.1103/PhysRevAccelBeams.25.122802	journal	December 2022
Transverse phase space tomography in an accelerator test facility using image compression and machine learning Wolski, A.; Johnson, M. A.; King, M. Physical Review Accelerators and Beams, Vol. 25, Issue 12 https://doi.org/10.1103/PhysRevAccelBeams.25.122803	journal	December 2022
High-Fidelity Prediction of Megapixel Longitudinal Phase-Space Images of Electron Beams Using Encoder-Decoder Neural Networks Zhu, J.; Chen, Y.; Brinker, F. Physical Review Applied, Vol. 16, Issue 2 https://doi.org/10.1103/PhysRevApplied.16.024005	journal	August 2021
Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator Scheinker, Alexander; Cropp, Frederick; Filippetto, Daniele Physical Review E, Vol. 107, Issue 4 https://doi.org/10.1103/PhysRevE.107.045302	journal	April 2023
Demonstration of Model-Independent Control of the Longitudinal Phase Space of Electron Beams in the Linac-Coherent Light Source with Femtosecond Resolution Scheinker, Alexander; Edelen, Auralee; Bohler, Dorian Physical Review Letters, Vol. 121, Issue 4 https://doi.org/10.1103/PhysRevLett.121.044801	journal	July 2018
Bayesian Optimization of a Free-Electron Laser Duris, J.; Kennedy, D.; Hanuka, A. Physical Review Letters, Vol. 124, Issue 12 https://doi.org/10.1103/PhysRevLett.124.124801	journal	March 2020
Model-independent particle accelerator tuning Scheinker, Alexander; Pang, Xiaoying; Rybarcyk, Larry Physical Review Special Topics - Accelerators and Beams, Vol. 16, Issue 10 https://doi.org/10.1103/physrevstab.16.102803	journal	October 2013
A unifying approach to extremum seeking: Adaptive schemes based on estimation of derivatives Nesic, D.; Tan, Y.; Moase, W. H. 49th IEEE Conference on Decision and Control (CDC) https://doi.org/10.1109/CDC.2010.5717929	conference	December 2010
Control barrier function based quadratic programs with application to adaptive cruise control Ames, Aaron D.; Grizzle, Jessy W.; Tabuada, Paulo 2014 IEEE 53rd Annual Conference on Decision and Control (CDC), 53rd IEEE Conference on Decision and Control https://doi.org/10.1109/CDC.2014.7040372	conference	December 2014
Semi-Global Practical Extremum Seeking with Practical Safety Williams, Alan; Krstic, Miroslav; Scheinker, Alexander 2023 62nd IEEE Conference on Decision and Control (CDC) https://doi.org/10.1109/CDC49753.2023.10383630	conference	December 2023
Practically Safe Extremum Seeking Williams, Alan; Krstic, Miroslav; Scheinker, Alexander 2022 IEEE 61st Conference on Decision and Control (CDC) https://doi.org/10.1109/CDC51059.2022.9993069	conference	December 2022
Control Barrier Function Based Quadratic Programs for Safety Critical Systems Ames, Aaron D.; Xu, Xiangru; Grizzle, Jessy W. IEEE Transactions on Automatic Control, Vol. 62, Issue 8 https://doi.org/10.1109/TAC.2016.2638961	journal	August 2017
Minimization of Betatron Oscillations of Electron Beam Injected Into a Time-Varying Lattice via Extremum Seeking Scheinker, Alexander; Huang, Xiaobiao; Wu, Juhao IEEE Transactions on Control Systems Technology, Vol. 26, Issue 1 https://doi.org/10.1109/TCST.2017.2664728	journal	January 2018
Extremum Seeking-Based Control System for Particle Accelerator Beam Loss Minimization Scheinker, Alexander; Huang, En-Chuan; Taylor, Charles IEEE Transactions on Control Systems Technology, Vol. 30, Issue 5 https://doi.org/10.1109/TCST.2021.3136133	journal	September 2022
Extremum-Seeking-Based Adaptive Model-Free Control and Its Application to Automated Vehicle Path Tracking Wang, Zejiang; Zhou, Xingyu; Wang, Junmin IEEE/ASME Transactions on Mechatronics, Vol. 27, Issue 5 https://doi.org/10.1109/TMECH.2022.3146727	journal	October 2022
On Non-Local Stability Properties of Extremum Seeking Control Tan, Y.; Nešić, D.; Mareels, I. M. Y. IFAC Proceedings Volumes, Vol. 38, Issue 1 https://doi.org/10.3182/20050703-6-CZ-1902.00747	journal	January 2005

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Title: Experimental Safe Extremum Seeking for Accelerators

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