Adaptive economic model predictive control: performance guarantees for nonlinear systems
File(s)
Accepted version
Author(s)
Degner, Maximilian
Soloperto, Raffaele
Zeilinger, Melanie N
Lygeros, John
Köhler, Johannes
Type
Journal Article
Abstract
We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control (MPC) framework that: (i) directly minimizes transient economic costs, (ii) addresses parametric uncertainty through online model adaptation, (iii) determines optimal setpoints online, and (iv) ensures robustness by using a tube-based approach. The proposed design ensures recursive feasibility, robust constraint satisfaction, and a transient performance bound. In case the disturbances have a finite energy and the parameter variations have a finite path length, the asymptotic average performance is (approximately) not worse than the performance obtained when operating at the best reachable steady-state. We highlight performance benefits in a numerical example involving a chemical reactor with unknown time-invariant and time-varying parameters.
Date Issued
2026-07-01
Date Acceptance
2026-01-01
Citation
IEEE Transactions on Automatic Control, 2026, 71 (7), pp.4355-4370
ISSN
0018-9286
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
4355
End Page
4370
Journal / Book Title
IEEE Transactions on Automatic Control
Volume
71
Issue
7
Copyright Statement
Copyright © 2026 Copyright Owner. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2026-01-19