258 citations · 2.4k across the 118 of their papers we have counts for
22 papers · 1 filter
Robust dynamic self-triggered control for nonlinear systems using hybrid Lyapunov functions
Michael Hertneck, Frank Allgöwer
Self-triggered control (STC) is a resource efficient approach to determine sampling instants for Networked Control Systems. At each sampling instant, an STC mechanism determines no…
Data-driven Control of Dynamic Event-triggered Systems with Delays
Xin Wang, Jian Sun, Julian Berberich +3
This paper studies data-driven control of unknown sampled-data systems with communication delays under an event-triggering transmission mechanism. Data-based representations for ti…
Dynamic self-triggered control for nonlinear systems based on hybrid Lyapunov functions
Michael Hertneck, Frank Allgöwer
Self-triggered control (STC) is a well-established technique to reduce the amount of samples for sampled-data systems, and is hence particularly useful for Networked Control System…
Data-driven system analysis of nonlinear systems using polynomial approximation
Tim Martin, Frank Allgöwer
In the context of data-driven control of nonlinear systems, many approaches lack of rigorous guarantees, call for nonconvex optimization, or require knowledge of a function basis c…
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…
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…
Data-driven model predictive control: closed-loop guarantees and experimental results
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured…
Robust output feedback model predictive control using online estimation bounds
Johannes Köhler, Matthias A. Müller, Frank Allgöwer
We present a framework to design nonlinear robust output feedback model predictive control (MPC) schemes that ensure constraint satisfaction under noisy output measurements and dis…
Linear tracking MPC for nonlinear systems Part II: The data-driven case
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We present a novel data-driven model predictive control (MPC) approach to control unknown nonlinear systems using only measured input-output data with closed-loop stability guarant…
Linear tracking MPC for nonlinear systems Part I: The model-based case
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We develop a tracking model predictive control (MPC) scheme for nonlinear systems using the linearized dynamics at the current state as a prediction model. Under reasonable assumpt…
Data-Driven Reachability Analysis from Noisy Data
Amr Alanwar, Anne Koch, Frank Allgöwer +1
We consider the problem of computing reachable sets directly from noisy data without a given system model. Several reachability algorithms are presented for different types of syst…
Model predictive control for linear uncertain systems using integral quadratic constraints
Lukas Schwenkel, Johannes Köhler, Matthias A. Müller +1
In this work, we propose a tube-based MPC scheme for state- and input-constrained linear systems subject to dynamic uncertainties characterized by dynamic integral quadratic constr…
Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliers
Patricia Pauli, Dennis Gramlich, Julian Berberich +1
In this paper, we analyze the stability of feedback interconnections of a linear time-invariant system with a neural network nonlinearity in discrete time. Our analysis is based on…
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…
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…
Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures
Tim Martin, Frank Allgöwer
In the context of dynamical systems, nonlinearity measures quantify the strength of nonlinearity by means of the distance of their input-output behaviour to a set of linear input-o…
Data-Based System Analysis and Control of Flat Nonlinear Systems
Mohammad Alsalti, Julian Berberich, Victor G. Lopez +2
Willems et al. showed that all input-output trajectories of a discrete-time linear time-invariant system can be obtained using linear combinations of time shifts of a single, persi…
Robust stability analysis of a simple data-driven model predictive control approach
Joscha Bongard, Julian Berberich, Johannes Köhler +1
In this paper, we provide a theoretical analysis of closed-loop properties of a simple data-driven model predictive control (MPC) scheme. The formulation does not involve any termi…
Transient Performance of Tube-based Robust Economic Model Predictive Control
Christian Klöppelt, Lukas Schwenkel, Frank Allgöwer +1
In this paper, we provide non-averaged and transient performance guarantees for recently developed, tube-based robust economic model predictive control (MPC) schemes. In particular…
Data-Driven Controller Design via Finite-Horizon Dissipativity
Nils Wieler, Julian Berberich, Anne Koch +1
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 clo…
On the design of terminal ingredients for data-driven MPC
Julian Berberich, Johannes Köhler, Matthias A. Müller +1
We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a term…
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…