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arXiv · 2511.21248

Stability of data-driven Koopman MPC with terminal conditions

Irene Schimperna·Lea Bold·Johannes Köhler·Karl Worthmann·Lalo Magni
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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.

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Irene Schimperna, Lea Bold, Johannes Köhler, Karl Worthmann, Lalo Magni. 2025-11-26. Stability of data-driven Koopman MPC with terminal conditions. https://arxiv.org/abs/2511.21248

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