Title data
Bold, Lea ; Grüne, Lars ; Schaller, Manuel ; Worthmann, Karl:
Data-driven MPC with stability guarantees using extended dynamic mode decomposition.
In: IEEE Transactions on Automatic Control.
Vol. Online First
(July 2024)
.
- pp. 1-8.
ISSN 1558-2523
DOI: https://doi.org/10.1109/TAC.2024.3431169
This is the latest version of this item.
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Project information
Project title: |
Project's official title Project's id Optimierungsbasierte Steuerung und Regelung 507037103 |
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Project financing: |
Deutsche Forschungsgemeinschaft |
Abstract in another language
For nonlinear (control) systems, extended dynamic mode decomposition (EDMD) is a popular method to obtain data-driven surrogate models. Its theoretical foundation is the Koopman framework, in which one propagates observable functions of the state to obtain a linear representation in an infinite-dimensional space. In this work, we prove practical asymptotic stability of a (controlled) equilibrium for EDMD-based model predictive control, in which the optimization step is conducted using the data-based surrogate model. To this end, we derive novel bounds on the estimation error that are proportional to the norm of state and control. This enables us to show that, if the underlying system is cost controllable, this stabilizablility property is preserved. We conduct numerical simulations illustrating the proven practical asymptotic stability.
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Available Versions of this Item
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Practical asymptotic stability of data-driven model predictive control using extended DMD. (deposited 03 Aug 2023 10:48)
- Data-driven MPC with stability guarantees using extended dynamic mode decomposition. (deposited 26 Jul 2024 08:42) [Currently Displayed]