@misc{BragardHueningKowalewski2023,
  author    = {Bragard, Michael and H{\"u}ning, Felix and Kowalewski, Paul},
  title     = {Vorrichtung zur Relativlagenbestimmung [Offenlegungschrift]},
  year      = {2023},
  abstract  = {Die Erfindung betrifft eine Vorrichtung zur Bestimmung einer Relativlage zwischen einem feststehenden Teil und einem zu demselben in eine Bewegungsrichtung bewegbaren beweglichen Teil, wobei der feststehende Teil mit einem Wiegandsensor versehen ist, wobei der Wiegandsensor zwischen zwei gegenpolig zueinander ausgebildeten Permanentmagneten angeordnet ist und dass der bewegliche Teil eine Mehrzahl von beabstandet zueinander angeordneten Magnetisierungsstegen aus einem magnetisch leitenden Material aufweist, die in der Bewegungsrichtung zumindest eine gleich große Erstreckung aufweisen wie der Permanentmagnet, dass ein Abstand zwischen benachbarten Magnetisierungsstegen derart gew{\"a}hlt ist, dass in einer ersten Relativlage ein erster Permanentmagnet von einem der Magnetisierungsstege {\"u}berdeckt ist und ein zweiter Permanentmagnet nicht von einem der Magnetisierungsstege {\"u}berdeckt ist.},
  language  = {de}
}
@article{KowalewskiBragardHueningetal.2023,
  author    = {Kowalewski, Paul and Bragard, Michael and H{\"u}ning, Felix and De Doncker, Rik W.},
  title     = {An inexpensive Wiegand-sensor-based rotary encoder without rotating magnets for use in electrical drives},
  series = {IEEE Transactions on Instrumentation and Measurement},
  journal   = {IEEE Transactions on Instrumentation and Measurement},
  publisher = {IEEE},
  issn      = {0018-9456 (Print)},
  doi       = {10.1109/TIM.2023.3326166},
  pages     = {10 Seiten},
  year      = {2023},
  abstract  = {This paper introduces an inexpensive Wiegand-sensor-based rotary encoder that avoids rotating magnets and is suitable for electrical-drive applications. So far, Wiegand-sensor-based encoders usually include a magnetic pole wheel with rotating permanent magnets. These encoders combine the disadvantages of an increased magnet demand and a limited maximal speed due to the centripetal force acting on the rotating magnets. The proposed approach reduces the total demand of permanent magnets drastically. Moreover, the rotating part is manufacturable from a single piece of steel, which makes it very robust and cheap. This work presents the theoretical operating principle of the proposed approach and validates its benefits on a hardware prototype. The presented proof-of-concept prototype achieves a mechanical resolution of 4.5 ° by using only 4 permanent magnets, 2Wiegand sensors and a rotating steel gear wheel with 20 teeth.},
  language  = {en}
}
@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}
}