Stability of data-driven Koopman MPC with terminal conditions
File(s)
Accepted version
Author(s)
Schimperna, Irene
Bold, Lea
Kohler, Johannes
Worthmann, Karl
Magni, Lalo
Type
Conference Paper
Abstract
This paper derives conditions under which Model Predictive Control (MPC) with terminal conditions, using a data-driven surrogate model as a prediction model, asymptotically stabilizes the plant despite approximation errors. In particular, we prove recursive feasibility and asymptotic stability if a proportional error bound holds, where proportional means that the bound is linear in the norm of the state and the input. For a broad class of nonlinear systems, this condition can be satisfied using data-driven surrogate models generated by kernel Extended Dynamic Mode Decomposition (kEDMD) using the Koopman operator. Last, the applicability of the proposed framework is demonstrated in a numerical case study.
Date Issued
2025-11-26
Date Acceptance
2026-03-05
Citation
2025
Copyright Statement
Subject to copyright. This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
Identifier
https://arxiv.org/abs/2511.21248v1
Source
European Control Conference
Subjects
eess.SY
eess.SY
math.OC