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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89701 |
| Date | 13 February 2024 |
| Creators | Wittig, M., Rütters, R., Bragard, M. |
| Contributors | Hochschule für Technik, Wirtschaft und Kultur Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-910103-02-3, urn:nbn:de:bsz:l189-qucosa2-896465, qucosa:89646 |
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