198 citations · 558 across the 21 of their papers we have counts for
27 papers
Stochastic Model Predictive Control Utilizing Bayesian Neural Networks
J. Pohlodek, H. Alsmeier, B. Morabito +3
Integrating measurements and historical data can enhance control systems through learning-based techniques, but ensuring performance and safety is challenging. Robust model predict…
LMI-based Data-Driven Robust Model Predictive Control
Hoang Hai Nguyen, Maurice Friedel, Rolf Findeisen
Predictive control, which is based on a model of the system to compute the applied input optimizing the future system behavior, is by now widely used. If the nominal models are not…
Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles
Johanna Bethge, Maik Pfefferkorn, Alexander Rose +2
We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. The proposed approach employs…
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics
Janine Matschek, Johanna Bethge, Rolf Findeisen
Many robotic tasks, such as human-robot interactions or the handling of fragile objects, require tight control and limitation of appearing forces and moments alongside sensible mot…
Towards a modeling, optimization and predictive control framework for fed-batch metabolic cybergenetics
Sebastián Espinel-Ríos, Bruno Morabito, Johannes Pohlodek +3
Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes \textit{extracellular} process element…
Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra
Joachim Schaeffer, Paul Gasper, Esteban Garcia-Tamayo +10
Analysis of Electrochemical Impedance Spectroscopy (EIS) data for electrochemical systems often consists of defining an Equivalent Circuit Model (ECM) using expert knowledge and th…
Machine Learning for Process Control of (Bio)Chemical Processes
Andreas Himmel, Janine Matschek, Rudolph Kok +3
The control of manufacturing processes must satisfy high quality and efficiency requirements while meeting safety requirements. A broad spectrum of monitoring and control strategie…
Safe Hierarchical Model Predictive Control and Planning for Autonomous Systems
Markus Koegel, Mohamed Ibrahim, Christian Kallies +1
Planning and control for autonomous vehicles usually are hierarchical separated. However, increasing performance demands and operating in highly dynamic environments requires an fr…
Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC
Johannes Pohlodek, Bruno Morabito, Christian Schlauch +2
Model-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used for decades in many engi…
Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference
Joe Watson, Hany Abdulsamad, Rolf Findeisen +1
Optimal control under uncertainty is a prevailing challenge for many reasons. One of the critical difficulties lies in producing tractable solutions for the underlying stochastic o…
Control of Scanning Quantum Dot Microscopy
Michael Maiworm, Christian Wagner, Taner Esat +4
Scanning quantum dot microscopy is a recently developed high-resolution microscopy technique that is based on atomic force microscopy and is capable of imaging the electrostatic po…
A Polynomial Chaos Approach to Robust Static Output-Feedback Control with Bounded Truncation Error
Yiming Wan, Dongying E. Shen, Sergio Lucia +2
This article considers the static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invarian…
Controller tuning in power systems using singular value optimization
Amer Mešanović, Ulrich Münz, Rolf Findeisen
As the share of renewable generation in large power systems continues to increase, the operation of power systems becomes increasingly challenging. The constantly shifting mix of r…
Fusing Online Gaussian Process-Based Learning and Control for Scanning Quantum Dot Microscopy
Maik Pfefferkorn, Michael Maiworm, Christian Wagner +2
Elucidating electrostatic surface potentials contributes to a deeper understanding of the nature of matter and its physicochemical properties, which is the basis for a wide field o…
Constrained Gaussian Process Learning for Model Predictive Control
Janine Matschek, Andreas Himmel, Kai Sundmacher +1
Many control tasks can be formulated as a tracking problem of a known or unknown reference signal. Examples are movement compensation in collaborative robotics, the synchronisation…
Learning References with Gaussian Processes in Model Predictive Control applied to Robot Assisted Surgery
Janine Matschek, Tim Gonschorek, Magnus Hanses +3
One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However,…
Scalable and Data Privacy Conserving Controller Tuning for Large-Scale Power Networks
Amer Mešanović, Ulrich Münz, Rolf Findeisen
The increasing share of renewable generation leads to new challenges in reliable power system operation, such as the rising volatility of power generation, which leads to time-vary…
Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees
Michael Maiworm, Daniel Limon, Rolf Findeisen
Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the un…
Structured Controller Parameter Tuning for Power Systems
A. Mesanovic, U. Münz, A. Szabo +6
Reliable and secure operation of power systems becomes increasingly challenging as the share of volatile generation rises, leading to largely changing dynamics. Typically, the arch…
Comments on Truncation Errors for Polynomial Chaos Expansions
Tillmann Mühlpfordt, Rolf Findeisen, Veit Hagenmeyer +1
Methods based on polynomial chaos expansion allow to approximate the behavior of systems with uncertain parameters by deterministic dynamics. These methods are used in a wide range…
Modeling metabolic networks including gene expression and uncertainties
Henning Lindhorst, Sergio Lucia, Rolf Findeisen +1
Constraint based methods, such as the Flux Balance Analysis, are widely used to model cellular growth processes without relying on extensive information on the regulatory features.…
Implementation of Nonlinear Model Predictive Path-Following Control for an Industrial Robot
Timm Faulwasser, Tobias Weber, Juan Pablo Zometa +1
Many robotic applications, such as milling, gluing, or high precision measurements, require the exact following of a pre-defined geometric path. In this paper, we investigate the r…
Nonlinear Model Predictive Control for Constrained Output Path Following
Timm Faulwasser, Rolf Findeisen
We consider the tracking of geometric paths in output spaces of nonlinear systems subject to input and state constraints without pre-specified timing requirements. Such problems ar…
Probabilistic and Set-based Model Invalidation and Estimation using LMIs
Stefan Streif, Didier Henrion, Rolf Findeisen
Probabilistic and set-based methods are two approaches for model invalidation, parameter and state estimation. Both classes of methods use different types of data, i.e. determinist…