258 citations · 1.2k across the 64 of their papers we have counts for
29 papers · 1 filter
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
Angela P. Schoellig, Matthias A. Müller, Sebastian Trimpe +8
Motivated by ten years of Learning-Based Control Invited Sessions at the IEEE Conference on Decision and Control (CDC), this tutorial paper reviews key advances at the intersection…
Utilizing Bayesian Optimization for Timetable-Independent Railway Junction Performance Determination
Tamme Emunds, Paul Brunzema, Sebastian Trimpe +1
The efficiency of railway infrastructure is significantly influenced by the mix of trains that utilize it, as different service types have competing operational requirements. While…
Local Bayesian Optimization for Controller Tuning with Crash Constraints
Alexander von Rohr, David Stenger, Dominik Scheurenberg +1
Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method…
Event-Triggered Safe Bayesian Optimization on Quadcopters
Antonia Holzapfel, Paul Brunzema, Sebastian Trimpe
Bayesian optimization (BO) has proven to be a powerful tool for automatically tuning control parameters without requiring knowledge of the underlying system dynamics. Safe BO metho…
Mean field limits for discrete-time dynamical systems via kernel mean embeddings
Christian Fiedler, Michael Herty, Sebastian Trimpe
Mean field limits are an important tool in the context of large-scale dynamical systems, in particular, when studying multiagent and interacting particle systems. While the continu…
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
Lukas Kesper, Sebastian Trimpe, Dominik Baumann
Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches requ…
Data-Driven Observability Analysis for Nonlinear Stochastic Systems
Pierre-François Massiani, Mona Buisson-Fenet, Friedrich Solowjow +2
Distinguishability and, by extension, observability are key properties of dynamical systems. Establishing these properties is challenging, especially when no analytical model is av…
Improving the Performance of Robust Control through Event-Triggered Learning
Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe
Robust controllers ensure stability in feedback loops designed under uncertainty but at the cost of performance. Model uncertainty in time-invariant systems can be reduced by recen…
Parameter Filter-based Event-triggered Learning
Sebastian Schlor, Friedrich Solowjow, Sebastian Trimpe
Model-based algorithms are deeply rooted in modern control and systems theory. However, they usually come with a critical assumption - access to an accurate model of the system. In…
On exploration requirements for learning safety constraints
Pierre-François Massiani, Steve Heim, Sebastian Trimpe
Enforcing safety for dynamical systems is challenging, since it requires constraint satisfaction along trajectory predictions. Equivalent control constraints can be computed in the…
Safe Value Functions
Pierre-François Massiani, Steve Heim, Friedrich Solowjow +1
Safety constraints and optimality are important, but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to mai…
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
Alexander von Rohr, Matthias Neumann-Brosig, Sebastian Trimpe
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. While learning-based con…
Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
Fabian Mager, Dominik Baumann, Carsten Herrmann +2
An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network. Examples include self-driv…
Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges
Dominik Baumann, Fabian Mager, Ulf Wetzker +3
Smart manufacturing aims to overcome the limitations of today's rigid assembly lines by making the material flow and manufacturing process more flexible, versatile, and scalable. T…
Learning Event-triggered Control from Data through Joint Optimization
Niklas Funk, Dominik Baumann, Vincent Berenz +1
We present a framework for model-free learning of event-triggered control strategies. Event-triggered methods aim to achieve high control performance while only closing the feedbac…
Event-triggered Learning for Linear Quadratic Control
Henning Schlüter, Friedrich Solowjow, Sebastian Trimpe
When models are inaccurate, the performance of model-based control will degrade. For linear quadratic control, an event-triggered learning framework is proposed that automatically…
Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees
Dominik Baumann, Fabian Mager, Romain Jacob +3
Closing feedback loops fast and over long distances is key to emerging cyber-physical applications; for example, robot motion control and swarm coordination require update interval…
Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems
José Mario Mastrangelo, Dominik Baumann, Sebastian Trimpe
A predictive triggering (PT) framework for the distributed control of resource constrained multi-agent systems is proposed. By predicting future communication demands and deriving…
Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems
Dominik Baumann, Fabian Mager, Marco Zimmerling +1
In future autonomous systems, wireless multi-hop communication is key to enable collaboration among distributed agents at low cost and high flexibility. When many agents need to tr…
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
Jonas Beuchert, Friedrich Solowjow, Jörg Raisch +2
Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and event-triggered learning methods reduce network communication by sending inf…
Event-triggered Learning
Friedrich Solowjow, Sebastian Trimpe
The efficient exchange of information is an essential aspect of intelligent collective behavior. Event-triggered control and estimation achieve some efficiency by replacing continu…
Event-triggered Pulse Control with Model Learning (if Necessary)
Dominik Baumann, Friedrich Solowjow, Karl Henrik Johansson +1
In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduc…
Resource-aware IoT Control: Saving Communication through Predictive Triggering
Sebastian Trimpe, Dominik Baumann
The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, fee…
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Matthias Neumann-Brosig, Alonso Marco, Dieter Schwarzmann +1
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model…