News

Adaptive Economic Model Predictive Control: Performance Guarantees for Nonlinear Systems
Maximilian Degner; Raffaele Soloperto; Melanie N. Zeilinger; John Lygeros; Johannes Köhler
IEEE Transactions on Automatic Control (TAC)

The work develops an adaptive economic MPC framework that optimizes economic performance under uncertainty via online adaptation and direct minimization of predicted economic cost. [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. [Paper]

Finite-sample-based reachability for safe control with Gaussian Process dynamics
Manish Prajapat, Johannes Köhler, Amon Lahr, Andreas Krause, Melanie N. Zeilinger
Automatica

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. [Paper]

A robust and adaptive MPC formulation for Gaussian process models
Mathieu Dubied, Amon Lahr, Melanie N. Zeilinger, Johannes Köhler
Automatica

This paper provides a predictive controller that ensures robust constraint satisfaction based on Gaussian process models that are adaptive using online data. [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. [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. [Paper]

Robust Convex Model Predictive Control With Collision Avoidance Guarantees for Robot Manipulators
Bernhard Wullt, Johannes Köhler, Per Mattsson, Mikael Norrlöf, and Thomas B. Schön,

Robust targeted exploration for systems with non-stochastic disturbances
Janani Venkatasubramanian, Johannes Köhler, Mark Cannon, Frank Allgöwer 
Automatica

The work develops a targeted exploration framework that guarantees parameter estimation accuracy under bounded disturbances, without relying on stochastic or i.i.d. noise models. [Paper]

Safety-aware load sharing in compressor stations using constrained Bayesian optimization
Liqiu Dong, Mohamed Becha, Johannes Köhler, Marta Zagorowska, Mehmet Mercangöz
Computers and Chemical Engineering

The paper provides a Bayesian optimization approach to operate load sharing for compressor stations while accounting for safety-critical surge-margin constraints.  [Paper]

A model predictive control framework with robust stability guarantees under unbounded disturbances

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. [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. [Preprint]

Full-order Safety for Nonlinear Reduced-Order MPC
Luis Pabon, Johannes Köhler, Revant Kasichainula, Marco Pavone

The paper provides an MPC that ensures safe operation using only a reduced-order model.  Significant improvements in computational cost and conservatism compared to existing certified approaches are demonstrated on large-scale simulation benchmarks, including a finite-element soft robot. [Preprint]

Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurements
Yibo Zhou, Johannes Köhler

The paper provides an algorithm to identify flexible nonlinearly parametrized models from partial and noisy measurements. The approach uses the prediction-error method (PEM) with moving horizon estimation (MHE) and improves scalability to large numbers of parameters. [Preprint]