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Es existieren verschiedenste Arten von Spielen, die versuchen, die Motivation einer Spielsituation in einen ernsten Kontext zu überführen. In diesem Artikel wird der Überbegriff „Competence Developing Games“ definiert und anhand von Beispielen erläutert. Dafür werden Erkennungskriterien vorgestellt, entsprechende Spieltypen erläutert und eine Zuordnung durch-geführt.
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.
Infused Thermal Solutions (ITS) introduces a method for passive thermal control to stabilize structural components thermally without active heating and cooling systems, but with phase change material (PCM) for thermal energy storage (TES), in combination with lattice - both embedded in additive manufactured functional structures. In this ITS follow-on paper a thermal model approach and associated predictions are presented, related on the ITS functional breadboards developed at FH Aachen. Predictive TES by PCM is provided by a specially developed ITS PCM subroutine, which is applicable in ESATAN. The subroutine is based on the latent heat storage (LHS) method to numerically embed thermo-physical PCM behavior. Furthermore, a modeling approach is introduced to numerically consider the virtual PCM/lattice nodes within the macro-encapsulated PCM voids of the double wall ITS design. Related on these virtual nodes, in-plane and out-of-plane conductive links are defined. The recent additive manufactured ITS breadboard series are thermally cycled in the thermal vacuum chamber, both with and without embedded PCM. Related on breadboard hardware tests, measurement results are compared with predictions and are subsequently correlated. The results of specific simulations and measurements are presented. Recent predictive results of star tracker analyses are also presented in ICES-2021-106, based on this ITS PCM subroutine.
Neue Perspektiven für die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation
(2019)
Deutschland braucht mehr Eisenbahn um CO2-Emissionen aus dem Verkehr zu reduzieren. Sie muss zum Rückgrat aktueller Logistikprozesse, z.B. bei Kaufmannsgütern und E-Commerce, werden. Dies geht nicht ohne neuartige betriebliche Konzepte und eine Transformation des Güterwagens von einem „dummen Stück Stahl“ zu einem modernen Werkzeug der Logistik.
Als „Güterwagen 4.0“ wird ein kommunikativer und kooperativer Güterwagen verstanden, der die Voraussetzung zur Automatisierung aller Prozesse der Zugvorbereitung bereitstellt, sich aber ansonsten vollkommen kompatibel mit heutigen Betriebsverfahren im Hauptlauf präsentiert. Durch Kommunikation zwischen Güterwagen und umgebenden intelligenten Systemen im Sinne eines „Internet der Dinge“ gelingt damit unter Anderem die Realisierung hoch effizienter Gleisanschlussverkehre, die der Güterbahn neue Märkte abseits der klassisch bahn-affinen Verkehre erschließen und letztlich den Wandel zu einer nachhaltigen Gütermobilität fördern.