Adaptive Optimal Trajectory Tracking Control Applied to a Large-Scale Ball-on-Plate System
Abstract
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.
Cite
@article{arxiv.2010.13486,
title = {Adaptive Optimal Trajectory Tracking Control Applied to a Large-Scale Ball-on-Plate System},
author = {Florian Köpf and Sean Kille and Jairo Inga and Sören Hohmann},
journal= {arXiv preprint arXiv:2010.13486},
year = {2021}
}
Comments
F. K\"opf and S. Kille contributed equally to this work. \c{opyright} 2021 IEEE