Stochastic MPC with online-optimized policies and closed-loop guarantees
File(s)
Accepted version
Author(s)
Bartos, Marcell
Didier, Alexandre
Sieber, Jerome
Kohler, Johannes
Zeilinger, Melanie N
Type
Journal Article
Abstract
This paper proposes a stochastic model predictive control method for linear systems affected by additive Gaussian disturbances that optimizes over disturbance feedback matrices online. Closed-loop satisfaction of probabilistic constraints and recursive feasibility of the underlying convex optimization problem is guaranteed. Optimization over feedback policies online increases performance and reduces conservatism compared to fixed-feedback approaches. The central mechanism is a finitely determined maximal admissible set for probabilistic constraints, together with the reconditioning of the predicted probabilistic constraints on the current knowledge at every time step. The proposed method's applicability is demonstrated on a building temperature control example.
Date Issued
2026-07-10
Date Acceptance
2026-07-06
Citation
IEEE Transactions on Automatic Control, 2026, pp.1-16
ISSN
0018-9286
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1
End Page
16
Journal / Book Title
IEEE Transactions on Automatic Control
Copyright Statement
Copyright © 2026, IEEE. 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 online
Date Publish Online
2026-07-10