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Solidification of silver-germanium alloys in an amorphous matrix aboard the space station Mir
(1993)
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.
Vermessung
(2006)
Vermessung
(2006)
Vermessung
(2016)
Biopharmaceuticals such as antibodies are produced in cultivated mammalian cells, which must be monitored to comply with good manufacturing practice. We, therefore, developed a fully automated system comprising a specific exhaust gas analyzer, inline analytics and a corresponding algorithm to precisely determine the oxygen uptake rate, carbon dioxide evolution rate, carbon dioxide transfer rate, transfer quotient and respiratory quotient without interrupting the ongoing cultivation, in order to assess its reproducibility. The system was verified using chemical simulation experiments and was able to measure the respiratory activity of hybridoma cells and DG44 cells (derived from Chinese hamster ovary cells) with satisfactory results at a minimum viable cell density of ~2.0 × 10⁵ cells ml⁻¹. The system was suitable for both batch and fed-batch cultivations in bubble-aerated and membrane-aerated reactors, with and without the control of pH and dissolved oxygen.