42 citations · 138 across the 22 of their papers we have counts for
12 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…
Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF
Tobin Holtmann, David Stenger, Andres Posada-Moreno +2
State estimation in control and systems engineering traditionally requires extensive manual system identification or data-collection effort. However, transformer-based foundation m…
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…
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…
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…
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…
Efficient Encoding of Dynamical Systems through Local Approximations
Friedrich Solowjow, Arash Mehrjou, Bernhard Schölkopf +1
An efficient representation of observed data has many benefits in various domains of engineering and science. Representing static data sets, such as images, is a living branch in m…
Event-triggered Learning for Resource-efficient Networked Control
Friedrich Solowjow, Dominik Baumann, Jochen Garcke +1
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the pre…