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Adaptive Optimal Trajectory Tracking Control Applied to a Large-Scale Ball-on-Plate System

Köpf, Florian; Kille, Sean; Inga, Jairo; Hohmann, Sören


While many theoretical works concerning Adaptive Dynamic Programming (ADP) have been proposed, application results are scarce. Therefore, we design an ADP-based optimal trajectory tracking controller and apply it to a large-scale ball-on-plate system. Our proposed method incorporates an approximated reference trajectory instead of using setpoint tracking and allows to automatically compensate for constant offset terms. Due to the off-policy characteristics of the algorithm, the method requires only a small amount of measured data to train the controller. Our experimental results show that this tracking mechanism significantly reduces the control cost compared to setpoint controllers. Furthermore, a comparison with a model-based optimal controller highlights the benefits of our model-free data-based ADP tracking controller, where no system model and manual tuning are required but the controller is tuned automatically using measured data.

DOI: 10.23919/ACC50511.2021.9482629
Zitationen: 1
Zitationen: 3
Zugehörige Institution(en) am KIT Institut für Regelungs- und Steuerungssysteme (IRS)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator KITopen-ID: 1000128916
Erschienen in Proceedings of the 2021 American Control Conference (ACC), 26th - 28th May, New Orleans, LA
Veranstaltung Annual American Control Conference (ACC 2021), New Orleans, LA, USA, 26.05.2021 – 28.05.2021
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Schlagwörter Adaptive Dynamic Programming, Reinforcement Learning, Tracking Controller, Control Application
Nachgewiesen in arXiv
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