Search Results for author: Anne Koch

Found 8 papers, 3 papers with code

Data-Driven Reachability Analysis from Noisy Data

1 code implementation15 May 2021 Amr Alanwar, Anne Koch, Frank Allgöwer, Karl Henrik Johansson

We consider the problem of computing reachable sets directly from noisy data without a given system model.

Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems

no code implementations30 Mar 2021 Chris Verhoek, Roland Tóth, Sofie Haesaert, Anne Koch

Based on the behavioural framework for LPV systems, we prove that one can obtain a result similar to Willems'.

LEMMA

Data-Driven Controller Design via Finite-Horizon Dissipativity

no code implementations15 Jan 2021 Nils Wieler, Julian Berberich, Anne Koch, Frank Allgöwer

Given one open-loop measured trajectory of a single-input single-output discrete-time linear time-invariant system, we present a framework for data-driven controller design for closed-loop finite-horizon dissipativity.

Offset-free setpoint tracking using neural network controllers

no code implementations23 Nov 2020 Patricia Pauli, Johannes Köhler, Julian Berberich, Anne Koch, Frank Allgöwer

In this paper, we present a method to analyze local and global stability in offset-free setpoint tracking using neural network controllers and we provide ellipsoidal inner approximations of the corresponding region of attraction.

Data-Driven Reachability Analysis Using Matrix Zonotopes

1 code implementation17 Nov 2020 Amr Alanwar, Anne Koch, Frank Allgöwer, Karl Henrik Johansson

In this paper, we propose a data-driven reachability analysis approach for unknown system dynamics.

Robust and optimal predictive control of the COVID-19 outbreak

no code implementations7 May 2020 Johannes Köhler, Lukas Schwenkel, Anne Koch, Julian Berberich, Patricia Pauli, Frank Allgöwer

Our theoretical findings support various recent studies by showing that 1) adaptive feedback strategies are required to reliably contain the COVID-19 outbreak, 2) well-designed policies can significantly reduce the number of fatalities compared to simpler ones while keeping the amount of social distancing measures on the same level, and 3) imposing stronger social distancing measures early on is more effective and cheaper in the long run than opening up too soon and restoring stricter measures at a later time.

Model Predictive Control

Training robust neural networks using Lipschitz bounds

2 code implementations6 May 2020 Patricia Pauli, Anne Koch, Julian Berberich, Paul Kohler, Frank Allgöwer

More specifically, we design an optimization scheme based on the Alternating Direction Method of Multipliers that minimizes not only the training loss of an NN but also its Lipschitz constant resulting in a semidefinite programming based training procedure that promotes robustness.

Model-Free Practical Cooperative Control for Diffusively Coupled Systems

no code implementations12 Jun 2019 Miel Sharf, Anne Koch, Daniel Zelazo, Frank Allgöwer

In this paper, we develop a data-based controller design framework for diffusively coupled systems with guaranteed convergence to an $\epsilon$-neighborhood of the desired formation.

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