@inproceedings{9d85db88cbdb4ba69b9ed355163b779f,
title = "Robust Constraint Satisfaction in Data-Driven MPC",
abstract = "We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data. The scheme predicts future trajectories based on data-dependent Hankel matrices, which span the full system behavior if the input is persistently exciting. This paper extends previous work on data-driven MPC by including a suitable constraint tightening which ensures that the closed-loop trajectory satisfies desired pointwise-in-time output constraints. Furthermore, we provide estimation procedures to compute system constants related to controllability and observability, which are required to implement the constraint tightening. The practicality of the proposed approach is illustrated via a numerical example. ",
keywords = "eess.SY, cs.SY, math.OC",
author = "Julian Berberich and Johannes K{\"o}hler and M{\"u}ller, \{Matthias A.\} and Frank Allg{\"o}wer",
note = "Funding Information: This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany{\textquoteright}s Excellence Strategy - EXC 2075 - 390740016. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Julian Berberich, and the International Research Training Group “Soft Tissue Robotics” (GRK 2198/1). ; 2020 59th IEEE Conference on Decision and Control (CDC) ; Conference date: 14-12-2020 Through 18-12-2020",
year = "2020",
doi = "10.1109/CDC42340.2020.9303965",
language = "English",
isbn = "978-1-7281-7446-4",
series = "Proceedings of the IEEE Conference on Decision and Control",
pages = "1260--1267",
booktitle = "2020 59th IEEE Conference on Decision and Control, CDC 2020",
}