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@PHDTHESIS{VonRohr:1006124,
      author       = {Von Rohr, Alexander},
      othercontributors = {Trimpe, Johann Sebastian and Hoos, Holger Hendrik},
      title        = {{P}robabilistic optimization for the control of dynamical
                      systems},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-02257},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2024},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2025; Dissertation, RWTH Aachen University, 2024},
      abstract     = {The control of dynamical systems is a central aspect of
                      automation technology, integral to modern society. However,
                      designing resource-efficient controllers specific to each
                      system remains challenging and costly. Machine learning can
                      automate this process, but learning-based control requires
                      methods that are both data-efficient and robust. This thesis
                      addresses the central question: "How can we efficiently
                      learn to control dynamical systems from data?" by framing
                      learning-based control as a probabilistic optimization
                      problem. We introduce novel methods for decision-making
                      under uncertainty that enable efficient data collection and
                      propose learning-based control algorithms for probabilistic
                      models with formal guarantees. First, we tackle the
                      controller tuning problem, which traditionally demands
                      extensive experimentation. We develop a Bayesian
                      optimization method for data-efficient improvement of
                      candidate controllers by reducing uncertainty in gradient
                      estimates. This method is validated through synthetic
                      problems, simulated experiments, and hardware controller
                      tuning. Second, we propose three probabilistically robust
                      control methods that account for uncertainties arising from
                      data-driven model learning. These include a controller
                      synthesis method with probabilistic stability guarantees,
                      leveraging the posterior distribution of learned models; an
                      approach addressing aleatoric uncertainty in systems with
                      inherent variations, deriving upper bounds on the minimum
                      data required to achieve quadratic stability; and an
                      event-triggered learning algorithm for uncertain and
                      time-varying dynamics, which detects system changes and
                      triggers re-learning. In summary, this thesis addresses the
                      challenge of efficiently learning to control dynamical
                      systems from data using probabilistic optimization. By
                      leveraging data-efficient probabilistic machine learning
                      techniques, we improve control performance while minimizing
                      resource consumption. The proposed methods are validated
                      through simulations and experiments.},
      cin          = {422610 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)422610_20200514$ / $I:(DE-82)120000_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-02257},
      url          = {https://publications.rwth-aachen.de/record/1006124},
}