Control of Unknown (Linear) Systems with Receding Horizon Learning
Abstract
A receding horizon learning scheme is proposed to transfer the state of a discrete-time dynamical control system to zero without the need of a system model. Global state convergence to zero is proved for the class of stabilizable and detectable linear time-invariant systems, assuming that only input and output data is available and an upper bound of the state dimension is known. The proposed scheme consists of a receding horizon control scheme and a proximity-based estimation scheme to estimate and control the closed-loop trajectory
Cite
Text
Ebenbauer et al. "Control of Unknown (Linear) Systems with Receding Horizon Learning." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Ebenbauer et al. "Control of Unknown (Linear) Systems with Receding Horizon Learning." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/ebenbauer2021l4dc-control/)BibTeX
@inproceedings{ebenbauer2021l4dc-control,
title = {{Control of Unknown (Linear) Systems with Receding Horizon Learning}},
author = {Ebenbauer, Christian and Pfitz, Fabian and Yu, Shuyou},
booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
year = {2021},
pages = {584-596},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/ebenbauer2021l4dc-control/}
}