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.
@article{arxiv.2003.06808,
title = {Robust Constraint Satisfaction in Data-Driven MPC},
author = {Julian Berberich and Johannes Köhler and Matthias A. Müller and Frank Allgöwer},
journal= {arXiv preprint arXiv:2003.06808},
year = {2021}
}
Comments
This version fixes a minor typo in the published version (definition of equilibrium on page 2, change marked in blue color)