@inproceedings{WittigRuettersBragard2024,
  author    = {Wittig, M. and R{\"u}tters, Ren{\´e} and Bragard, Michael},
  title     = {Application of RL in control systems using the example of a rotatory inverted pendulum},
  series = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  booktitle = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  editor    = {Reiff-Stephan, J{\"o}rg and J{\"a}kel, Jens and Schwarz, Andr{\´e}},
  publisher = {le-tex publishing services GmbH},
  address   = {Leipzig},
  isbn      = {978-3-910103-02-3},
  doi       = {10.33968/2024.53},
  pages     = {241 -- 248},
  year      = {2024},
  abstract  = {In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.},
  language  = {en}
}
@article{FaganBitzBjoerkmanBurtscheretal.2021,
  author    = {Fagan, Andrew J. and Bitz, Andreas and Bj{\"o}rkman-Burtscher, Isabella M. and Collins, Christopher M. and Kimbrell, Vera and Raaijmakers, Alexander J. E.},
  title     = {7T MR Safety},
  series = {Journal of Magnetic Resonance Imaging (JMRI)},
  volume    = {53},
  journal   = {Journal of Magnetic Resonance Imaging (JMRI)},
  number    = {2},
  publisher = {Wiley},
  address   = {Weinheim},
  issn      = {1522-2586},
  doi       = {10.1002/jmri.27319},
  pages     = {333 -- 346},
  year      = {2021},
  language  = {en}
}
@article{FiedlerLaddClemensetal.2020,
  author    = {Fiedler, Thomas M. and Ladd, Mark E. and Clemens, Markus and Bitz, Andreas},
  title     = {Safety of subjects during radiofrequency exposure in ultra-high-field magnetic resonance imaging},
  series = {IEEE Letters on Electromagnetic Compatibility Practice and Applications},
  volume    = {2},
  journal   = {IEEE Letters on Electromagnetic Compatibility Practice and Applications},
  number    = {3},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {2637-6423},
  doi       = {10.1109/LEMCPA.2020.3029747},
  pages     = {1 -- 8},
  year      = {2020},
  abstract  = {Magnetic resonance imaging (MRI) is one of the most important medical imaging techniques. Since the introduction of MRI in the mid-1980s, there has been a continuous trend toward higher static magnetic fields to obtain i.a. a higher signal-to-noise ratio. The step toward ultra-high-field (UHF) MRI at 7 Tesla and higher, however, creates several challenges regarding the homogeneity of the spin excitation RF transmit field and the RF exposure of the subject. In UHF MRI systems, the wavelength of the RF field is in the range of the diameter of the human body, which can result in inhomogeneous spin excitation and local SAR hotspots. To optimize the homogeneity in a region of interest, UHF MRI systems use parallel transmit systems with multiple transmit antennas and time-dependent modulation of the RF signal in the individual transmit channels. Furthermore, SAR increases with increasing field strength, while the SAR limits remain unchanged. Two different approaches to generate the RF transmit field in UHF systems using antenna arrays close and remote to the body are investigated in this letter. Achievable imaging performance is evaluated compared to typical clinical RF transmit systems at lower field strength. The evaluation has been performed under consideration of RF exposure based on local SAR and tissue temperature. Furthermore, results for thermal dose as an alternative RF exposure metric are presented.},
  language  = {en}
}
@inproceedings{HofmannLimpertMatareetal.2019,
  author    = {Hofmann, Till and Limpert, Nicolas and Matar{\´e}, Viktor and Ferrein, Alexander and Lakemeyer, Gerhard},
  title     = {Winning the RoboCup Logistics League with Fast Navigation, Precise Manipulation, and Robust Goal Reasoning},
  series = {RoboCup 2019: Robot World Cup XXIII. RoboCup},
  booktitle = {RoboCup 2019: Robot World Cup XXIII. RoboCup},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-35699-6},
  doi       = {10.1007/978-3-030-35699-6_41},
  pages     = {504 -- 516},
  year      = {2019},
  language  = {en}
}
@inproceedings{HoegenDonckerRuetters2020,
  author    = {Hoegen, Anne von and Doncker, Rik W. De and R{\"u}tters, Ren{\´e}},
  title     = {Teaching Digital Control of Operational Amplifier Processes with a LabVIEW Interface and Embedded Hardware},
  series = {The 23rd International Conference on Electrical Machines and Systems (ICEMS), Hamamatsu, Japan},
  booktitle = {The 23rd International Conference on Electrical Machines and Systems (ICEMS), Hamamatsu, Japan},
  doi       = {10.23919/ICEMS50442.2020.9290928},
  pages     = {1117 -- 1122},
  year      = {2020},
  language  = {en}
}
@incollection{IbanezSanchezWolf2020,
  author    = {Ibanez-Sanchez, Gema and Wolf, Martin},
  title     = {Interactive Process Mining-Induced Change Management Methodology for Healthcare},
  series = {Interactive Process Mining in Healthcare},
  booktitle = {Interactive Process Mining in Healthcare},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-53993-1 (Online)},
  doi       = {10.1007/978-3-030-53993-1_16},
  pages     = {267 -- 293},
  year      = {2020},
  abstract  = {The adoption of the Digital Health Transformation is a tremendous paradigm change in health organizations, which is not a trivial process in reality. For that reason, in this chapter, it is proposed a methodology with the objective to generate a changing culture in healthcare organisations. Such a change culture is essential for the successful implementation of any supporting methods like Interactive Process Mining. It needs to incorporate (mostly) new ways of team-based and evidence-based approaches for solving structural problems in a digital healthcare environment.},
  language  = {en}
}
@article{HueningBackes2020,
  author    = {H{\"u}ning, Felix and Backes, Andreas},
  title     = {Direct observation of large Barkhausen jump in thin Vicalloy wires},
  series = {IEEE Magnetics Letters},
  volume    = {11},
  journal   = {IEEE Magnetics Letters},
  number    = {Art. 2506504},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {1949-307X},
  doi       = {10.1109/LMAG.2020.3046411},
  pages     = {1 -- 4},
  year      = {2020},
  language  = {en}
}
@inproceedings{HoegenDonckerBragardetal.2021,
  author    = {Hoegen, Anne von and Doncker, Rik W. De and Bragard, Michael and Hoegen, Svenja von},
  title     = {Problem-Based Learning in Automation Engineering: Performing a Remote Laboratory Session Serving Various Educational Attainments},
  series = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  booktitle = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  doi       = {10.1109/EDUCON46332.2021.9453925},
  pages     = {1605 -- 1614},
  year      = {2021},
  language  = {en}
}
@inproceedings{HueningStuettgen2021,
  author    = {H{\"u}ning, Felix and St{\"u}ttgen, Marcel},
  title     = {Work in Progress: Interdisciplinary projects in times of COVID-19 crisis - challenges, risks and chances},
  series = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  booktitle = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  doi       = {10.1109/EDUCON46332.2021.9454006},
  pages     = {1175 -- 1179},
  year      = {2021},
  language  = {en}
}
@article{AltherrEdererLorenzetal.2014,
  author    = {Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F. and P{\"o}ttgen, Philipp},
  title     = {Experimental validation of an enhanced system synthesis approach},
  series = {Operations Research Proceedings 2014},
  journal   = {Operations Research Proceedings 2014},
  editor    = {L{\"u}bbecke, Marco and Koster, Arie and Letmathe, Peter and Madlener, Reihard and Peis, Britta and Walther, Grit},
  publisher = {Springer},
  address   = {Basel},
  isbn      = {978-3-319-28695-2},
  doi       = {10.1007/978-3-319-28697-6_1},
  pages     = {6},
  year      = {2014},
  abstract  = {Planning the layout and operation of a technical system is a common task for an engineer. Typically, the workflow is divided into consecutive stages: First, the engineer designs the layout of the system, with the help of his experience or of heuristic methods. Secondly, he finds a control strategy which is often optimized by simulation. This usually results in a good operating of an unquestioned sys- tem topology. In contrast, we apply Operations Research (OR) methods to find a cost-optimal solution for both stages simultaneously via mixed integer program- ming (MILP). Technical Operations Research (TOR) allows one to find a provable global optimal solution within the model formulation. However, the modeling error due to the abstraction of physical reality remains unknown. We address this ubiq- uitous problem of OR methods by comparing our computational results with mea- surements in a test rig. For a practical test case we compute a topology and control strategy via MILP and verify that the objectives are met up to a deviation of 8.7\%.},
  language  = {en}
}