Inhaltsangabe
This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. This book focuses on the global control and optimization layers. As a basis of global control, the beam feedback system regulates the beam parameters against disturbances and stabilizes them around the setpoints. The global optimization algorithms, such as the robust conjugate direction search algorithm, genetic algorithm, and particle swarm optimization algorithm, are at the top layer, determining the feedback setpoints for optimal beam qualities.
In addition, the authors also introduce the applications of machine learning for beam controls. Selected machine learning algorithms, such as supervised learning based on artificial neural networks and Gaussian processes, and reinforcement learning, are discussed. They are applied to configure feedback loops, accelerate global optimizations, and directly synthesize optimal controllers. Authors also demonstrate the effectiveness of these algorithms using either simulation or tests at the SwissFEL. With this book, the readers gain systematic knowledge of intelligent beam controls and learn the layered architecture guiding the design of practical beam control systems.
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Über die Autorin bzw. den Autor
Zheqiao Geng is a senior electronic engineer at the Paul Scherrer Institute in Switzerland. He graduated with a bachelor’s degree from Tsinghua University in Beijing, China, in 2002. In 2007 he received his Ph.D. degree in nuclear engineering from the Graduate School of Chinese Academy of Sciences. For more than ten years, he worked on accelerator RF and beam control systems in different labs, including IHEP (China), DESY (Germany), SLAC (USA) and PSI (Switzerland). He was the key developer of critical aspects of the LLRF systems for various accelerator projects such as the European XFEL, LCLS and SwissFEL. At SLAC, he led the system-level design of the LCLS-II LLRF system. At PSI, he developed the control strategy for setting up and stabilizing the two-bunch operation of SwissFEL. As an internationally acclaimed RF and beam control expert, he was appointed as a PSI Senior Expert in 2021.
Stefan Simrock is a control system coordinator atthe ITER Organization located in southern France. He studied physics and microwave engineering at the Technical University of Darmstadt where he received his Ph.D. in engineering physics in 1988. From 1988 to 1996, he worked at the Thomas Jefferson National Accelerator Facility as a RF controls group leader and the deputy for the technical performance of the accelerator. He joined DESY in 1996 as the leader of a multidisciplinary team responsible for the design, construction, and commissioning of the control system for the superconducting linac at the TESLA Test Facility. In 2004, he was an appointed group leader of beam controls group responsible for the timing, synchronization, and beam feedback systems of all 10 accelerators at DESY. At the same time, he was the project leader for the RF Control System for FLASH and the European XFEL. Since 2010, he is responsible for the integration of ITER diagnostics with the central control system, machine protection system, safety system, andplasma control system.
Together with Dr. Zheqiao Geng, he published a book “Low-Level Radio Frequency Systems (978-3-030-94418-6)” in the series of Particle Acceleration and Detection in 2022.
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This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. This book focuses on the global control and optimization layers. As a basis of global control, the beam feedback system regulates the beam parameters against disturbances and stabilizes them around the setpoints. The global optimization algorithms, such as the robust conjugate direction search algorithm, genetic algorithm, and particle swarm optimization algorithm, are at the top layer, determining the feedback setpoints for optimal beam qualities.
In addition, the authors also introduce the applications of machine learning for beam controls. Selected machine learning algorithms, such as supervised learning based on artificial neural networks and Gaussian processes, and reinforcement learning, are discussed. They are applied to configure feedback loops, accelerate global optimizations, and directly synthesize optimal controllers. Authors also demonstrate the effectiveness of these algorithms using either simulation or tests at the SwissFEL. With this book, the readers gain systematic knowledge of intelligent beam controls and learn the layered architecture guiding the design of practical beam control systems.
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Taschenbuch. Zustand: Neu. Intelligent Beam Control in Accelerators | Zheqiao Geng (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer | EAN 9783031285998 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 129317326
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Intelligent Beam Control in Accelerators
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book systematically discusses the algorithms and principles for achieving stable and optimal beam (or products of the beam) parameters in particle accelerators. A four-layer beam control strategy is introduced to structure the subsystems related to beam controls, such as beam device control, beam feedback, and beam optimization. This book focuses on the global control and optimization layers. As a basis of global control, the beam feedback system regulates the beam parameters against disturbances and stabilizes them around the setpoints. The global optimization algorithms, such as the robust conjugate direction search algorithm, genetic algorithm, and particle swarm optimization algorithm, are at the top layer, determining the feedback setpoints for optimal beam qualities.In addition, the authors also introduce the applications of machine learning for beam controls. Selected machine learning algorithms, such as supervised learning based on artificial neural networks and Gaussian processes, and reinforcement learning, are discussed. They are applied to configure feedback loops, accelerate global optimizations, and directly synthesize optimal controllers. Authors also demonstrate the effectiveness of these algorithms using either simulation or tests at the SwissFEL. With this book, the readers gain systematic knowledge of intelligent beam controls and learn the layered architecture guiding the design of practical beam control systems. Artikel-Nr. 9783031285998
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