News & updates from the group
New TAC paper
Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems
Maximilian Degner; Raffaele Soloperto; Melanie N. Zeilinger; John Lygeros; Johannes Köhler,
The work develops an adaptive economic MPC framework that optimizes economic performance under uncertainty via online adaptation and direct minimization of predicted economic cost.
ACE AI & Data Talk
Data-driven models in predictive control: Uncertainty quantification and robust designs
Talk in the ACE AI & Data Talk series on data-driven approaches in predictive control, focusing on uncertainty quantification and robust design methods.
Recording available online:
https://www.youtube.com/watch?v=MM1YzfgMQKE
New preprint
Certainty-equivalent adaptive MPC for uncertain nonlinear systems
We developed an adaptive model predictive control (MPC) scheme with online learning for time-varying systems. The method enables online adaptation while maintaining robustness and guarantees on tracking performance and constraint satisfaction.
New preprint
Finite-sample bounds for multi-output system identification
Léo Simpson, Katrin Baumgärtner, Johannes Köhler, Moritz Diehl
The paper derives finite-sample confidence bounds for linear regression in the multi-output setting. The results extend self-normalized martingale bounds beyond scalar outputs and provide tighter confidence sets for system identification.
New L-CSS paper
Exponential stability of data-driven nonlinear MPC using input/output models
Lea Bold, Irene Schimperna, Karl Worthmann, and Johannes Köhler, IEEE Control Systems Letters (L-CSS)
The work shows that learned surrogate models can enable model predictive control with exponential stability guarantees for unknown nonlinear systems.
New L-CSS paper
Our group at European Control Conference (ECC) 2026
Tutorial session
Safe-by-design control using robust MPC
Slides available online: https://kohlerjohannes.github.io/ECC_Tutorial_SafeByDesignMPC
Workshop presentations
Stochastic and data-driven MPC
https://www.tuhh.de/ics/workshops-seminars/ecc2026-stochastic-control
https://uac-ecc26.github.io/schedule/
Invited session (organiser)
Advances in MPC: safe decision-making under uncertainty
Conference talk
MPC with reduced-order models
https://arxiv.org/abs/2511.03002
New preprint
New Automatica paper
Finite-sample-based reachability for safe control with Gaussian Process dynamics
Manish Prajapat, Johannes Köhler, Amon Lahr, Andreas Krause, Melanie N. Zeilinger
This work provides a method to ensure safe operation using Gaussian process models in a model predictive control framework by over-approximating the reachable set based on predictions from a finite number of GP samples.
https://www.sciencedirect.com/science/article/pii/S0005109826003882
New Automatica paper
A robust and adaptive MPC formulation for Gaussian process models
Mathieu Dubied, Amon Lahr, Melanie N. Zeilinger, Johannes Köhler
This paper provides a predictive controller that ensures robust constraint satisfaction based on Gaussian process models that are adaptive using online data.
https://arxiv.org/abs/2507.02098
New EJC paper
Robust reduced-order model predictive control using peak-to-peak analysis of filtered signals
Johannes Kohler, Carlo Scholz, Melanie Zeilinger
European Journal of Control (EJC)
This work presents a predictive control method for reduced-order models that retains safety guarantees and reduces conservatism by over four orders of magnitude compared to existing approaches.
https://www.sciencedirect.com/science/article/pii/S0947358026001226?
New TAC paper
Stochastic MPC with Online-optimized Policies and Closed-loop Guarantees
Marcell Bartos, Alexandre Didier, Jerome Sieber, Johannes Köhler, and Melanie N. Zeilinger
IEEE Transactions on Automatic Control (TAC)
The work develops a stochastic MPC framework that optimizes over closed-loop policies to minimize expected cost while providing guarantees for probabilistic constraint satisfaction in infinite-horizon operation.