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@INPROCEEDINGS{Appel:358352,
      author       = {Appel, Sabrina and Hirlaender, Simon and Madysa, Nico},
      editor       = {Pilat, Fulvia and Fischer, Wolfram and Saethre, Robert and
                      Anisimov, Petr and Andrian, Ivan},
      title        = {{D}ata-driven model predictive control for automated
                      optimization of injection into the {SIS}18 synchrotron},
      address      = {Geneva, Switzerland},
      publisher    = {JACoW Publishing},
      reportid     = {GSI-2025-00536},
      pages        = {1800 - 1803},
      year         = {2024},
      note         = {Published by JACoW Publishing under the terms of the
                      Creative Commons Attribution 4.0 license.},
      abstract     = {In accelerator labs like GSI/FAIR, automating complex
                      systems is key for maximizing physics experiment time. This
                      study explores the application of a data-driven model
                      predictive control (MPC) to refine the multi-turn injection
                      (MTI) process into the SIS18 synchrotron, departing from
                      conventional numerical optimization methods. MPC is
                      distinguished by its reduced number of optimization steps
                      and superior ability to control performance criteria,
                      effectively addressing issues like delayed outcomes and
                      safety concerns, including septum protection. The study
                      focuses on a highly sample-efficient MPC approach based on
                      Gaussian processes, which lies at the intersection of
                      model-based reinforcement learning and control theory. This
                      approach merges the strengths of both fields, offering a
                      unified and optimized solution and yielding a safe and fast
                      state-based optimization approach beyond classical
                      reinforcement learning and Bayesian optimization. Our study
                      lays the groundwork for enabling safe online training for
                      the SS18 MTI issue, showing great potential for applying
                      data-driven control in similar scenarios.},
      month         = {May},
      date          = {2024-05-19},
      organization  = {15th International Particle
                       Accelerator Conference, Nashville,
                       Tennessee (USA), 19 May 2024 - 24 May
                       2024},
      keywords     = {Accelerator Physics (Other) /
                      mc6-beam-instrumentation-controls-feedback-and-operational-aspects
                      - MC6: Beam Instrumentation, Controls, Feedback, and
                      Operational Aspects (Other) / MC6.D13 - MC6.D13 Machine
                      Learning (Other)},
      cin          = {APH},
      cid          = {I:(DE-Ds200)APH-20060809OR090},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      experiment   = {$EXP:(DE-Ds200)no_experiment-20200803$},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.18429/JACOW-IPAC2024-TUPS59},
      url          = {https://repository.gsi.de/record/358352},
}