A robust and adaptive MPC formulation for Gaussian process models
File(s)
Published version
Author(s)
Dubied, Mathieu
Lahr, Amon
Zeilinger, Melanie
Kohler, Johannes
Type
Journal Article
Abstract
In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded disturbances and unmodeled nonlinearities. We use Gaussian Processes (GPs) to learn the uncertain dynamics based on noisy measurements, including those collected during system operation. As a key contribution, we derive robust predictions for GP models using contraction metrics, which are incorporated in the MPC formulation. The proposed
design guarantees recursive feasibility, robust constraint satisfaction and convergence to a reference state, with high probability. We provide a numerical example of a planar quadrotor subject to difficult-to-model ground effects, which highlights significant improvements achieved through the proposed robust prediction method and through online learning.
design guarantees recursive feasibility, robust constraint satisfaction and convergence to a reference state, with high probability. We provide a numerical example of a planar quadrotor subject to difficult-to-model ground effects, which highlights significant improvements achieved through the proposed robust prediction method and through online learning.
Date Issued
2026-12-01
Date Acceptance
2026-07-17
Citation
Automatica, 2026, 194
ISSN
0005-1098
Publisher
Elsevier
Journal / Book Title
Automatica
Volume
194
Copyright Statement
© 2026 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.automatica.2026.113276
Publication Status
Published
Article Number
113276
Date Publish Online
2026-09-04