258 citations · 2.4k across the 118 of their papers we have counts for
10 papers · 1 filter
An overview of systems-theoretic guarantees in data-driven model predictive control
Julian Berberich, Frank Allgöwer
The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretic…
On discount functions for economic model predictive control without terminal conditions
Lukas Schwenkel, Daniel Briem, Matthias A. Müller +1
In this paper, we investigate discounted economic model predictive control (E-MPC) schemes without terminal conditions in scenarios where the optimal operating behavior is a period…
How improving performance may imply losing consistency in event-triggered consensus
David Meister, Duarte J. Antunes, Frank Allgöwer
Event-triggered control is often argued to lower the average triggering rate compared to time-triggered control while still achieving a desired control goal, e.g., the same perform…
Lipschitz constant estimation for general neural network architectures using control tools
Patricia Pauli, Dennis Gramlich, Frank Allgöwer
This paper is devoted to the estimation of the Lipschitz constant of general neural network architectures using semidefinite programming. For this purpose, we interpret neural netw…
Data-Driven Min-Max MPC for Linear Systems: Robustness and Adaptation
Yifan Xie, Julian Berberich, Frank Allgöwer
Data-driven controllers design is an important research problem, in particular when data is corrupted by the noise. In this paper, we propose a data-driven min-max model predictive…
Bootstrapping Guarantees: Stability and Performance Analysis for Dynamic Encrypted Control
Sebastian Schlor, Frank Allgöwer
Encrypted dynamic controllers that operate for an unlimited time have been a challenging subject of research. The fundamental difficulty is the accumulation of errors and scaling f…
Collision Avoidance Safety Filter for an Autonomous E-Scooter using Ultrasonic Sensors
Robin Strässer, Marc Seidel, Felix Brändle +4
In this paper, we propose a collision avoidance safety filter for autonomous electric scooters to enable safe operation of such vehicles in pedestrian areas. In particular, we empl…
State space representations of the Roesser type for convolutional layers
Patricia Pauli, Dennis Gramlich, Frank Allgöwer
From the perspective of control theory, convolutional layers (of neural networks) are 2-D (or N-D) linear time-invariant dynamical systems. The usual representation of convolutiona…
SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
Robin Strässer, Manuel Schaller, Karl Worthmann +2
The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode…
Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations
Patricia Pauli, Aaron Havens, Alexandre Araujo +4
Recently, semidefinite programming (SDP) techniques have shown great promise in providing accurate Lipschitz bounds for neural networks. Specifically, the LipSDP approach (Fazlyab…