activity
20182022
most citedData-driven analysis and control of continuous-time systems under aperiodic sampling

65 citations · 66 across the 12 of their papers we have counts for

collaborators

13 papers

eess.SY2022

Self-triggered MPC robust to bounded packet loss via a min-max approach: extended version

Stefan Wildhagen, Matthias Pezzutto, Luca Schenato +1

Networked Control Systems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law. A method tha…

eess.SY2021

Uncertainties and output feedback in rollout event-triggered control

Stefan Wildhagen, Frank Allgöwer

The fact that event-triggered control (ETC) often exhibits an improved performance-communication tradeoff over time-triggered control renders it especially useful for Networked Con…

eess.SY2021

Rollout event-triggered control: reconciling event- and time-triggered control

Stefan Wildhagen, Frank Dürr, Frank Allgöwer

Event-triggered control (ETC) and time-triggered control (TTC), the classical concepts to determine the transmission instants for networked control systems, each come with drawback…

eess.SY2021

Multi-party computation enables secure polynomial control based solely on secret-sharing

Sebastian Schlor, Michael Hertneck, Stefan Wildhagen +1

Encrypted control systems allow to evaluate feedback laws on external servers without revealing private information about state and input data, the control law, or the plant. While…

eess.SY2021

Improved stability conditions for systems under aperiodic sampling: model- and data-based analysis

Stefan Wildhagen, Julian Berberich, Matthias Hirche +1

Discrete-time systems under aperiodic sampling may serve as a modeling abstraction for a multitude of problems arising in cyber-physical and networked control systems. Recently, mo…

eess.SY2021

Data-driven analysis and controller design for discrete-time systems under aperiodic sampling

Stefan Wildhagen, Julian Berberich, Michael Hertneck +1

This article is concerned with data-driven analysis of discrete-time systems under aperiodic sampling, and in particular with a data-driven estimation of the maximum sampling inter…

math.OC2020★ 65 cited

Data-driven analysis and control of continuous-time systems under aperiodic sampling

Julian Berberich, Stefan Wildhagen, Michael Hertneck +1

We investigate stability analysis and controller design of unknown continuous-time systems under state-feedback with aperiodic sampling, using only noisy data but no model knowledg…

eess.SY2020

Rollout scheduling and control for disturbed systems via tube MPC: extended version

Stefan Wildhagen, Frank Allgöwer

Rollout control is an MPC-based control method, in which a controller is periodically activated to schedule the transmission of sensor or actuator data. Therein, a preassigned traf…

eess.SY2019

Saving Tokens in Rollout Control with Token Bucket Specification

Florian Jaumann, Stefan Wildhagen, Frank Allgöwer

We consider a communication network over which transmissions must fulfill the so-called token bucket traffic specification, with a rollout (i.e., predictive) controller that both s…

eess.SY2019

A Resource-Aware Approach to Self-Triggered Model Predictive Control: Extended Version

Stefan Wildhagen, Colin N. Jones, Frank Allgöwer

In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sa…

eess.SY2019

Scheduling and control over networks using MPC with time-varying terminal ingredients: extended version

Stefan Wildhagen, Frank Allgöwer

Rollout approaches are an effective tool to address the problem of co-designing the transmission schedule and the corresponding input values, when the controller is connected to th…

eess.SY2019

Economic MPC using a Cyclic Horizon with Application to Networked Control Systems

Stefan Wildhagen, Matthias A. Müller, Frank Allgöwer

In this paper, we analyze an economic model predictive control scheme with terminal region and cost, where the system is optimally operated in a certain subset of the state space.…

eess.SY2018★ 1 cited

Characterizing the learning dynamics in extremum seeking

Stefan Wildhagen, Simon Michalowsky, Jan Feiling +1

We consider perturbation-based extremum seeking, which recovers an approximate gradient of an analytically unknown objective function through measurements. Using classical needle v…