258 citations · 2.4k across the 118 of their papers we have counts for
134 papers
Learning and Control Beyond Linearity: Towards a Non-asymptotic Theory for Bilinear Systems
Yahya Sattar, Yassir Jedra, Robin Strässer +3
This tutorial provides a unified view of the emerging area of bilinear learning and control. Using linear systems as a benchmark, it explains what fundamentally changes in the bili…
Comparing Controller Synthesis Methods with Deadline-Miss Awareness
Melanie Gallant, Marc Seidel, Paolo Pazzaglia +5
This paper presents a comparative study of real-time controller design methods for dynamical control systems subject to deadline overruns. Numerous techniques have been proposed to…
Event-Triggered Discrete-Time Multivariable Extremum Seeking Systems
Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Miroslav Krstić +1
This paper introduces a discrete-time event-triggered extremum seeking framework for real-time optimization of multivariable nonlinear systems. In contrast to conventional discrete…
Verifiable computations for dynamic encrypted control
Sebastian Schlor, Frank Allgöwer
Encrypted control can preserve the privacy of data and parameters while the necessary computations can be outsourced to a cloud server. To ensure the integrity of the received valu…
Koopman meets input-output data: Data-driven output-feedback control of nonlinear systems with closed-loop guarantees
Robin Strässer, Julian Berberich, Manuel Schaller +2
Data-driven control of nonlinear systems from input-output measurements remains a fundamental challenge, as existing approaches with rigorous closed-loop guarantees predominantly r…
A Koopman Set-Membership Approach for Nonlinear Data-Driven Control with Stability Guarantees
Yifan Xie, Zuxun Xiong, Julian Berberich +2
This paper proposes a data-driven controller design method for unknown nonlinear systems based on a Koopman bilinear realization. Using Koopman operator theory, the nonlinear syste…
Data-Driven Min-Max MPC with Integral Quadratic Constraints
Yifan Xie, Julian Berberich, Frank Allgöwer
Data-driven control of nonlinear systems with rigorous guarantees is a challenging control problem. Integral quadratic constraints (IQCs) provide a powerful framework for modeling…
Beyond Bounded Noise: Stochastic Set-Membership Estimation for Nonlinear Systems
Felix Brändle, Nicolas Chatzikiriakos, Andrea Iannelli +1
In this paper, we derive a novel procedure for set-membership estimation of dynamical systems affected by stochastic noise with unbounded support. Employing a bound on the sample c…
A Controller Synthesis Framework for Weakly-Hard Control Systems
Marc Seidel, Martina Maggio, Frank Allgöwer
Deadline misses are more common in real-world systems than one may expect. The weakly-hard task model has become a standard abstraction to describe and analyze how often these miss…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
We survey classical, machine learning, and data-driven system identification approaches to learn control-relevant and physics-informed models of dynamical systems. Recently, machin…
IMMPC: An Internal Model Based MPC for Rejecting Unknown Disturbances
Felix Brändle, Frank Allgöwer
Model predictive control (MPC) is a powerful control method that allows for the direct inclusion of state and input constraints into the controller design. However, errors in the m…
Performance analysis for cone-preserving switched systems with constrained switching
Marc Seidel, Richard Pates, Frank Allgöwer
This paper studies cone-preserving linear discrete-time switched systems whose switching is governed by an automaton. For this general system class, we present performance analysis…
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…
Towards targeted exploration for non-stochastic disturbances
Janani Venkatasubramanian, Johannes Köhler, Mark Cannon +1
We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i.e., without requiring independence or zero mean, a…
Koopman-based feedback design with stability guarantees
Robin Strässer, Manuel Schaller, Karl Worthmann +2
We present a method to design a state-feedback controller ensuring exponential stability for nonlinear systems using only measurement data. Our approach relies on Koopman-operator…