IPAC2018 - List of Authors (Scheinker, A.)

Paper	Title	Page
WEPAL046	A New Digital Feedback and Feedforward Controller for Cavity Field Control of the LANSCE Accelerator	2277
	S. Kwon, L.J. Castellano, D.J. Knapp, J.T.M. Lyles, M.S. Prokop, A. Scheinker, P.A. Torrez LANL, Los Alamos, New Mexico, USA
	Funding: Work Supported by DOE A new digital low-level RF system was designed and has been deployed on the drift-tube-linac section of the Los Alamos Neutron Science Center(LANSCE) proton accelerator. This new system is part of a modernization of the existing analog cavity-field controls that were originally developed and put into service forty-five years ago. For stabilization of the cavity field amplitude and phase during beam loading, a proportional-integral feedback controller, a static beam feedforward controller, and an iterative learning controller working in parallel have been implemented. In this paper, the controller architecture is described, and the performances of the three controllers when beam is being actively accelerated is presented.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAL046
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THYGBE1	Applying Artificial Intelligence to Accelerators	2925
	A. Scheinker, R.W. Garnett, D. Rees LANL, Los Alamos, New Mexico, USA D.K. Bohler SLAC, Menlo Park, California, USA A.L. Edelen, S.V. Milton CSU, Fort Collins, Colorado, USA
	Particle accelerators are being designed and operated over a wide range of complex beam phase space distributions. For example, the Linac Coherent Light Source (LCLS) upgrade, LCLS-II, is considering complex schemes such as two-color operation [1], while the plasma wake field acceleration facility for advanced accelerator experimental tests (FACET) upgrade, FACET-II, is planning on providing custom tailored current profiles [2]. Because of uncertainty due to limited diagnostics and time varying performance, such as thermal drifts, as well as collective effects and the complex coupling of large numbers of components, it is impossible to use simple look up tables for parameter settings in order to quickly switch between widely varying operating ranges. Several forms of artificial intelligence are currently being investigated in order to enable accelerators to quickly and automatically re-adjust component settings without human intervention. In this work we discuss recent progress in applying neural networks and adaptive feedback algorithms to enable automatic accelerator tuning and optimization. [1] A. A. Lutman et al., Nat. Photonics 10.11, 745 (2016). [2] V. Yakimenko et al., IPAC2016, Busan, Korea, 2016.
	Slides THYGBE1 [14.256 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE1
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Paper

Title

Page

A New Digital Feedback and Feedforward Controller for Cavity Field Control of the LANSCE Accelerator

2277

S. Kwon, L.J. Castellano, D.J. Knapp, J.T.M. Lyles, M.S. Prokop, A. Scheinker, P.A. Torrez
LANL, Los Alamos, New Mexico, USA

Funding: Work Supported by DOE
A new digital low-level RF system was designed and has been deployed on the drift-tube-linac section of the Los Alamos Neutron Science Center(LANSCE) proton accelerator. This new system is part of a modernization of the existing analog cavity-field controls that were originally developed and put into service forty-five years ago. For stabilization of the cavity field amplitude and phase during beam loading, a proportional-integral feedback controller, a static beam feedforward controller, and an iterative learning controller working in parallel have been implemented. In this paper, the controller architecture is described, and the performances of the three controllers when beam is being actively accelerated is presented.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-WEPAL046

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THYGBE1

Applying Artificial Intelligence to Accelerators

2925

A. Scheinker, R.W. Garnett, D. Rees
LANL, Los Alamos, New Mexico, USA
D.K. Bohler
SLAC, Menlo Park, California, USA
A.L. Edelen, S.V. Milton
CSU, Fort Collins, Colorado, USA

Particle accelerators are being designed and operated over a wide range of complex beam phase space distributions. For example, the Linac Coherent Light Source (LCLS) upgrade, LCLS-II, is considering complex schemes such as two-color operation [1], while the plasma wake field acceleration facility for advanced accelerator experimental tests (FACET) upgrade, FACET-II, is planning on providing custom tailored current profiles [2]. Because of uncertainty due to limited diagnostics and time varying performance, such as thermal drifts, as well as collective effects and the complex coupling of large numbers of components, it is impossible to use simple look up tables for parameter settings in order to quickly switch between widely varying operating ranges. Several forms of artificial intelligence are currently being investigated in order to enable accelerators to quickly and automatically re-adjust component settings without human intervention. In this work we discuss recent progress in applying neural networks and adaptive feedback algorithms to enable automatic accelerator tuning and optimization.
[1] A. A. Lutman et al., Nat. Photonics 10.11, 745 (2016).
[2] V. Yakimenko et al., IPAC2016, Busan, Korea, 2016.

Slides THYGBE1 [14.256 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2018-THYGBE1

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)