TY - CHAP A1 - Wittig, M. A1 - Rütters, René A1 - Bragard, Michael ED - Reiff-Stephan, Jörg ED - Jäkel, Jens ED - Schwarz, André T1 - Application of RL in control systems using the example of a rotatory inverted pendulum T2 - Tagungsband AALE 2024 : Fit für die Zukunft: praktische Lösungen für die industrielle Automation N2 - 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. KW - Rotatory Inverted Pendulum KW - MPC KW - LQR KW - PPO KW - Reinforcement Learning Y1 - 2024 SN - 978-3-910103-02-3 U6 - https://doi.org/10.33968/2024.53 N1 - 20. AALE-Konferenz. Bielefeld, 06.03.-08.03.2024. (Tagungsband unter https://doi.org/10.33968/2024.29) SP - 241 EP - 248 PB - le-tex publishing services GmbH CY - Leipzig ER - TY - CHAP A1 - Altherr, Lena A1 - Döring, Bernd A1 - Frauenrath, Tobias A1 - Groß, Rolf A1 - Mohan, Nijanthan A1 - Oyen, Marc A1 - Schnittcher, Lukas A1 - Voß, Norbert ED - Reiff-Stephan, Jörg ED - Jäkel, Jens ED - Schwarz, André T1 - DiggiTwin: ein interdisziplinäres Projekt zur Nutzung digitaler Zwillinge auf dem Weg zu einem klimaneutralen Gebäudebestand T2 - Tagungsband AALE 2024 : Fit für die Zukunft: praktische Lösungen für die industrielle Automation N2 - Im Hinblick auf die Klimaziele der Bundesrepublik Deutschland konzentriert sich das Projekt Diggi Twin auf die nachhaltige Gebäudeoptimierung. Grundlage für eine ganzheitliche Gebäudeüberwachung und -optimierung bildet dabei die Digitalisierung und Automation im Sinne eines Smart Buildings. Das interdisziplinäre Projekt der FH Aachen hat das Ziel, ein bestehendes Hochschulgebäude und einen Neubau an klimaneutrale Standards anzupassen. Im Rahmen des Projekts werden bekannte Verfahren, wie das Building Information Modeling (BIM), so erweitert, dass ein digitaler Gebäudezwilling entsteht. Dieser kann zur Optimierung des Gebäudebetriebs herangezogen werden, sowie als Basis für eine Erweiterung des Bewertungssystems Nachhaltiges Bauen (BNB) dienen. Mithilfe von Sensortechnologie und künstlicher Intelligenz kann so ein präzises Monitoring wichtiger Gebäudedaten erfolgen, um ungenutzte Energieeinsparpotenziale zu erkennen und zu nutzen. Das Projekt erforscht und setzt methodische Erkenntnisse zu BIM und digitalen Gebäudezwillingen praxisnah um, indem es spezifische Fragen zur Energie- und Ressourceneffizienz von Gebäuden untersucht und konkrete Lösungen für die Gebäudeoptimierung entwickelt. KW - Anomalieerkennung KW - IoT KW - Überwachung & Optimierung KW - DiggiTwin KW - BIM KW - Smart Building KW - Digitalisierung Y1 - 2024 SN - 978-3-910103-02-3 U6 - https://doi.org/10.33968/2024.67 N1 - 20. AALE-Konferenz. Bielefeld, 06.03.-08.03.2024 (Tagungsband unter https://doi.org/10.33968/2024.29) SP - 341 EP - 346 PB - le-tex publishing services GmbH CY - Leipzig ER - TY - CHAP A1 - Kramer, Pia A1 - Bragard, Michael A1 - Ritz, Thomas A1 - Ferfer, Ute A1 - Schiffers, Tim T1 - Visualizing, Enhancing and Predicting Students’ Success through ECTS Monitoring T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - This paper serves as an introduction to the ECTS monitoring system and its potential applications in higher education. It also emphasizes the potential for ECTS monitoring to become a proactive system, supporting students by predicting academic success and identifying groups of potential dropouts for tailored support services. The use of the nearest neighbor analysis is suggested for improving data analysis and prediction accuracy. KW - Monitoring KW - Engineering education KW - Data visualization KW - Accuracy KW - Data analysis Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578652 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Rütters, René A1 - Bragard, Michael A1 - Dolls, Sarah T1 - The Inverted Rotary Pendulum: Facilitating Practical Teaching in Advanced Control Engineering T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - This paper outlines a practical approach to teach control engineering principles, with an inverted rotary pendulum, serving as an illustrative example. It shows how the pendulum is embedded in an advanced course of control engineering. This approach is incorporated into a flipped-classroom concept, as well as classical teaching concepts, offering students practical experience in control engineering. In addition, the design of the pendulum is shown, using a Raspberry Pi as the target platform for Matlab Simulink. This pendulum can be used in the classroom to evaluate the controller design mentioned above. It is analysed if the use of the pendulum generates a deeper understanding of the learning contents. KW - Matlab KW - Engineering education KW - Online services KW - Software packages KW - Electronic learning KW - Control engineering Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578937 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Becker, Tim A1 - Bragard, Michael T1 - Low-Voltage DC Training Lab for Electric Drives - Optimizing the Balancing Act Between High Student Throughput and Individual Learning Speed T2 - 2024 IEEE Global Engineering Education Conference (EDUCON) N2 - After a brief introduction of conventional laboratory structures, this work focuses on an innovative and universal approach for a setup of a training laboratory for electric machines and drive systems. The novel approach employs a central 48 V DC bus, which forms the backbone of the structure. Several sets of DC machine, asynchronous machine and synchronous machine are connected to this bus. The advantages of the novel system structure are manifold, both from a didactic and a technical point of view: Student groups can work on their own performance level in a highly parallelized and at the same time individualized way. Additional training setups (similar or different) can easily be added. Only the total power dissipation has to be provided, i.e. the DC bus balances the power flow between the student groups. Comparative results of course evaluations of several cohorts of students are shown. KW - Synchronous machines KW - Power dissipation KW - Throughput KW - Low voltage KW - DC machines KW - Manifolds KW - Training Y1 - 2024 U6 - https://doi.org/10.1109/EDUCON60312.2024.10578902 SN - 2165-9559 SN - 2165-9567 (eISSN) N1 - 2024 IEEE Global Engineering Education Conference (EDUCON), 08-11 May 2024, Kos Island, Greece PB - IEEE CY - New York, NY ER -