Robust and efficient data-driven predictive control

authored by

Mohammad Salahaldeen Ahmad Alsalti, Manuel Barkey, Victor Gabriel Lopez Mejia, Matthias A. Müller

Abstract

We propose a robust and efficient data-driven predictive control (eDDPC) scheme which is more sample efficient (requires less offline data) compared to existing schemes, and is also computationally efficient. This is done by leveraging an alternative data-based representation of the trajectories of linear time-invariant (LTI) systems. The proposed scheme relies only on using (short and potentially irregularly measured) noisy input-output data, the amount of which is independent of the prediction horizon. To account for measurement noise, we provide a novel result that quantifies the uncertainty between the true (unknown) restricted behavior of the system and the estimated one from noisy data. Furthermore, we show that the robust eDDPC scheme is recursively feasible and that the resulting closed-loop system is practically stable. Finally, we compare the performance of this scheme to existing ones on a case study of a four tank system.

Organisation(s)

Institute of Automatic Control

Type

Working paper/Discussion paper

No. of pages

17

Publication date

27.09.2024

Publication status

E-pub ahead of print

Electronic version(s)

https://doi.org/10.48550/arXiv.2409.18867 (Access: Open)