News & updates from the group

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

Johannes Köhler

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.

https://arxiv.org/abs/2603.17843

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.

https://arxiv.org/abs/2603.19073

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.

https://ieeexplore.ieee.org/document/11546700/

New L-CSS paper

Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
A Lahr, A Scampicchio, J Köhler, MN Zeilinger
Optimal error bounds for non-parametric regression under bounded noise are formulated as a Gaussian Process using duality

 

Our group at European Control Conference (ECC) 2026

Tutorial session
Safe-by-design control using robust MPC
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

A model-free approach to control barrier functions for higher-order systems
L Lanza, J Köhler, D Dennstädt, T Berger, K Worthmann
 
Control barrier functions (CBFs) are designed without a model using concepts from funnel control and appropriate modifications for higher-order systems.