198 citations · 558 across the 21 of their papers we have counts for
17 papers · 1 filter
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…
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 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…
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…
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…
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…