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Das von Texas-Instruments (TI) vertriebene Interface CBL2 wird über einige TI-Grafik-Rechner (TI-92, TI-89 usw.) angesteuert. Wegen seiner Handlichkeit wird dieses System beim Unterricht in wechselnden Räumen »großen« Messwerterfassungssystemen oft vorgezogen. Das CBL2 bietet drei analoge Eingänge, die immerhin mit 10 Bit Auflösung und bis zu einer Frequenz von 50 kHz arbeiten. Weiterhin besitzt das CBL2 eine Buchse für angeblich nur einen digitalen Ein- bzw. Ausgang. An diesem Eingang wird standardmäßig hauptsächlich der Bewegungssensor CBR betrieben. In diesem Beitrag werden Erweiterungsmöglichkeiten dieses Anschlusses beschrieben.
Wir stellen einen USB-Baustein vor, der eine kostengünstige und universelle Möglichkeit schafft , im Unterricht den Themenkreis Messen-Steuern-Regeln zu behandeln. Die Funktionalität orientiert sich am CVK-Interface der Firma Fischertechnik. Im Gegensatz zu kommerziellen Lösungen erlaubt unser Aufbau auch den preiswerten Einsatz in Gruppen- oder Einzelarbeit. Abschließend berichten wir über ein Beispiel aus dem Unterrichtseinsatz.
This paper covers the use of the magnetic Wiegand effect to design an innovative incremental encoder. First, a theoretical design is given, followed by an estimation of the achievable accuracy and an optimization in open-loop operation.
Finally, a successful experimental verification is presented. For this purpose, a permanent magnet synchronous machine is controlled in a field-oriented manner, using the angle information of the prototype.
Der Internally Commutated Thyristor (ICT) : ein neuartiger GCT mit integrierter Ausschalteinheit
(2007)
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.