Rolf Findeisen

Rolf Findeisen
Technische Universität Darmstadt | TU · Department of Electrical Engineering and Information Technology (Dept.18)

Prof. Dr.-Ing.

About

441
Publications
81,099
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
9,318
Citations
Introduction
Research Interests and vision Systems become increasingly complex and interconnected. They need to react autonomously to changes and disturbances. Control and systems theory is an enabling technology towards the analysis, safe operation and interoperation of such systems . The research of my group aims towards the development of theoretical sound control methods for intelligent, safe interconnected autonomous systems operating in dynamically changing environments.
Additional affiliations
May 2012 - October 2012
Massachusetts Institute of Technology
Position
  • Visiting Professor and Scholar
January 2012 - May 2012
École Polytechnique Fédérale de Lausanne
Position
  • Professor
October 2007 - August 2016
Otto-von-Guericke-Universität Magdeburg
Position
  • Chair for Systems Theory and Automatic Control

Publications

Publications (441)
Preprint
Full-text available
Agent-based simulations have become a popular and powerful tool for simulating emergent mobility modes. Often times, the memory and computing requirements are daunting. Scaling down agent populations by simulating only a fraction of all agents is a frequently used option to reduce these burdens. However, recent studies have pointed out the difficul...
Article
Machine learning methods, like Gaussian process regression, allow improving the performance of model-based control methods, such as model predictive control. They can, for example, be used to improve the prediction quality of the used model of the system, learning the uncertain system part. However, fusing model-based approaches with machine learni...
Article
Environmental and economic pressure leads to an increasing desire to operate processes over various operational conditions, adapting to changing conditions such as feed quality, available energy, customer demand, or product prices. This requires frequent changes in the process setpoints, involving transitions between those. We focus on the explicit...
Preprint
Full-text available
Planning and control for autonomous vehicles usually are hierarchical separated. However, increasing performance demands and operating in highly dynamic environments requires an frequent re-evaluation of the planning and tight integration of control and planning to guarantee safety. We propose an integrated hierarchical predictive control and plann...
Preprint
Full-text available
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 engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model-based approaches. Thanks to recent technologi...
Article
This note considers the H_infinity static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invariant parametric uncertainties. By applying polynomial chaos theory, the control synthesis problem is solved using a high-dimensional expanded system which characterizes stochastic state...
Article
Competitive biotechnological processes need to operate over various conditions and adapt to changing economic contexts. Dynamic ATP turnover allows trading off declines in biomass formation and volumetric productivity for enhancements of product yields in fermentations where the product pathway is linked to ATP synthesis. To facilitate its practica...
Article
Model-based control of biotechnological processes is, in general, challenging. Often the processes are complex, nonlinear, and uncertain. Hence modeling tends to be complex and is often inaccurate. For this reason, non-model-based control strategies developed via fask, bench-scale, or pilot plant experiments are often applied in the biotechnology i...
Article
We present a constrained model‐based optimization and predictive control framework to maximize the production efficiency of batch fermentations based on the core idea of manipulating adenosine triphosphate (ATP) wasting. In many bioprocesses, enforced ATP wasting —rerouting ATP use towards an energetically possibly suboptimal path— allows increasin...
Article
This article introduces a novel calorimetric measurement method, namely the ‘Double Pulse Method’, to measure reversible heat in lithium-ion battery cells. In Li-ion cells, reversible heat has a material-dependent characteristic as it is closely related to both entropy change and the temperature dependence of the open circuit voltage. The proposed...
Article
H2 static and dynamic output-feedback control problems are investigated for linear time-invariant uncertain systems. The goal is to minimize the averaged H2 performance in the presence of nonlinear dependence on time-invariant probabilistic parametric uncertainties. By applying the polynomial chaos theory, the control synthesis problem is solved us...
Article
Full-text available
The determination of the monomer fractions in polyhydroxyalkanoates is of great importance for research on microbial-produced plastic material. The development of new process designs, the validation of mathematical models, and intelligent control strategies for production depend enormously on the correctness of the analyzed monomer fractions. Most...
Article
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-varying dynamics and behavior of the system. To counteract the changing dynamics, we propose to adapt the parameters of existing controllers to the changing conditions. Doing...
Preprint
Full-text available
Optimal control under uncertainty is a prevailing challenge in control, due to the difficulty in producing tractable solutions for the stochastic optimization problem. By framing the control problem as one of input estimation, advanced approximate inference techniques can be used to handle the statistical approximations in a principled and practica...
Preprint
Full-text available
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 potential of nanostructures like molecules or single atoms. Recently, it could be shown that it not only yields qualitatively but also quantitatively cutting edge images ev...
Preprint
Full-text available
This article considers the $\mathcal{H}_\infty$ static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invariant parametric uncertainties. By applying polynomial chaos theory, the control synthesis problem is solved using a high-dimensional expanded system which characterizes stoc...
Article
Full-text available
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 underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data‐driven and machine learning approach...
Chapter
Compared to conventional petroleum-based polymers, biopolymers like polyhydroxyalkanoates are a promising alternative as raw material for manufacturing of plastics. One way to reduce the microbial PHA production costs is to use organic wastes from agriculture and food industries, which contain a wide range of carbon sources. Two organic waste carbo...
Article
Designing predictive controllers for systems with computationally limited embedded hardware, e.g. for autonomous vehicles, requires solving an optimization problem in real-time taking the vehicle dynamics and constraints into account. Furthermore, often the controller needs to be available in explicit form for verification and validation purposes a...
Article
Model predictive control requires the real-time solution of an optimal control problem, which can be challenging on computationally limited systems. Approximating the solution such as by neural networks or series expansions, or deriving an explicit solution, can overcome this challenge. Using neural networks for approximation, a question arises as...
Article
Often, systems need to adapt their behavior to other systems in their surroundings while obeying constraints to achieve good performance or due to safety reasons. We consider repetitive applications, where the reference for the controller stems from noisy sensor data. Including preview information of the reference, e.g. extrapolating from previous...
Article
We consider the control of constrained, uncertain systems over wireless communication networks with varying capabilities. We focus on the case that a radio resource manager, which manages the wireless network, provides guaranteed minimum communication at fixed time instants while offering more frequent communication depending on the current load of...
Article
Full-text available
Many processes operate repetitively, for example batch processes in biotechnology or chemical engineering. We propose a method for risk-aware run-to-run optimization and model predictive control of repetitive processes with uncertain models. The goal is to increase the performance as the number of runs increases by improving the model despite limit...
Article
There is a steadily increasing demand for full and partial autonomous operation of systems. One way to achieve autonomy for systems is the fusion of classical control approaches with methods from machine learning and artificial intelligence. We consider machine learning approaches to learn unknown or partially known references to increase the auton...
Article
This article discusses how to use optimization-based methods to efficiently operate microgrids with a large share of renewables. We discuss how to apply a frequency-based method to tune the droop parameters in order to stabilize the grid and improve oscillation damping after disturbances. Moreover, we propose a centralized real-time feasible nonlin...
Chapter
Safety critical control problems often require the availability of fallback strategies, in case of failure of the main control scheme, sensors or actuators. Those controllers should provide safe operation or emergency shut down of the system under all circumstance. They should also be able to operate subject to reduced information, and limited comp...
Conference Paper
Full-text available
Model Predictive Control has proven to be a universal and flexible method to control complex nonlinear system with guaranteed constraint satisfaction. However, high dependency on model quality often renders it inappropriate for hard to model systems. On the other hand, machine learning methods show great performance when approximating functions bas...
Article
Reliable and secure operation of power systems becomes increasingly challenging as the share of volatile generation rises, leading to largely changing dynamics. Typically, the architecture and structure of controllers in power systems, such as voltage controllers of power generators, are fixed during the design and buildup of the network. As replac...
Preprint
Full-text available
This article discusses how to use optimization-based methods to efficiently operate microgrids with a large share of renewables. We discuss how to apply a frequency-based method to tune the droop parameters in order to stabilize the grid and improve oscilation damping after disturbances. Moreover, we propose a centralized real-time feasible nonline...
Preprint
Full-text available
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 renewable and conventional generation leads to largely changing dynamics, increasing the risk of blackouts. We propose to retune the parameters of the already present cont...
Preprint
Full-text available
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 of applications. Scanning quantum dot microscopy, a recently developed technique allows to measure such potentials with atomic resolution. For an efficient deployment in s...
Article
Control of biotechnological processes is currently recipe-based with insufficient ability to handle possible uncertainties, which results in suboptimal production processes. To address this problem, model-based optimization and control approaches can be implemented to derive optimal control strategies. However, for reliable performance of model-bas...
Article
Full-text available
Robust control of uncertain nonlinear systems subject to constrants often leads to conservatism. Such behaviors can be improved by updating the model of the uncertainty with the data collected during the operation time or by bounding the parameters. This paper proposes an approach to robustly control the discrete-time Lur’e system subject to states...
Article
We consider robust tube based model predictive control of discrete time, constrained, linear systems subject to additive disturbances. Standard tube based approaches utilize as an auxiliary control law a single, fixed feedback/gain to counteract the effect of the future disturbances in the predictions. The fictive - never applied - control law allo...
Article
Control design and state estimation are usually more straightforward for linear than for nonlinear dynamical systems, which has motivated the development of methods for quantifying the extent of nonlinearity in dynamical systems. Although many well-defined methods have been proposed for systems described by ordinary differential equations, such met...
Article
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 renewable and conventional generation leads to largely changing dynamics, increasing the risk of blackouts. We propose to retune the parameters of the already present cont...
Article
This paper addresses Lyapunov characterizations of input-to-state stability for nonlinear switched discrete-time systems via finite-step Lyapunov functions with respect to closed sets. The use of finite-step Lyapunov functions permits not-necessarily input-to-state stable systems in the systems family, while input-to-state stability of the resultin...
Article
Full-text available
In upstream Oil and Gas operations a well is drilled following a planned trajectory. The trajectory is designed to avoid hard formations and other wells while minimizing drilling time. The uncertainty of the environment, e.g. unknown rock hardness, effects negatively the efficiency of operation: drilling time increases due to frequent corrective co...
Article
Implementing optimal controllers on embedded systems can be challenging as it requires the solution of an optimization problem in real-time. Furthermore, the a priory verification of stability, e.g. not relying on the possibly numerical solution of an optimization problem is often not possible. We propose a non-linear control synthesis based on an...
Article
Model Predictive Control has proven to be a universal and flexible method to control complex nonlinear system with guaranteed constraint satisfaction. However, high dependency on model quality often renders it inappropriate for hard to model systems. On the other hand, machine learning methods show great performance when approximating functions bas...
Article
Full-text available
Dynamic models of biotechnological processes form the basis of process optimization, control, and estimation. Metabolic network models are often at the core of such models. Since metabolic network models can be very large, and consequently computationally expensive, model reduction techniques can be applied. The derivation of a suitable reduced met...
Article
Planning and control of autonomous vehicles are becoming increasingly important for many applications. However, autonomous vehicles are often subject to disturbances and uncertainties, which become critical especially in cluttered and dynamic environments. To provide guaranteed constraints satisfaction, e.g. for collision avoidance, we propose a hi...
Article
Full-text available
Safe autonomous passing of intersections with mixed traffic, including human drivers and autonomous vehicles, is challenging. We propose a tailored approach that provides guarantees despite uncertainties fusing learned models and model predictive control. A single autonomous vehicle is controlled by the predictive controller via acceleration and st...
Article
Many control tasks can be formulated as tracking problems of a known or unknown reference signal. examples are motion compensation in collaborative robotics, the synchronisation of oscillations for power systems or the reference tracking of recipes in chemical process operation. Both the tracking performance and the stability of the closed-loop sys...
Article
We fix errata encountered in letter T. Mühlpfordt et al. “Comments on Truncation Errors for Polynomial Chaos Expansions”. In: IEEE Control Systems Letters 2.1 (2018), pp. 169–174.
Article
Full-text available
Background Interleukin-6 is a pleiotropic cytokine with high clinical relevance and an important mediator of cellular communication, orchestrating both pro- and anti-inflammatory processes. Interleukin-6-induced signalling is initiated by binding of IL-6 to the IL-6 receptor α and subsequent binding to the signal transducing receptor subunit gp130....
Article
In this work the Warburg impedance of an 18650 Li-ion cell is investigated in the time domain for frequencies down to 0.1 mHz. The measurement data is transformed into the frequency domain and the results are compared to electrochemical impedance spectroscopy (EIS) measurements. It is found that both measurement methods lead to the same result for...
Preprint
Full-text available
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 of oscillations for power systems or reference tracking of recipes in chemical process operation. Tracking performance as well as guaranteeing stability of the closed lo...
Preprint
Full-text available
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, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive prediction models can be u...
Preprint
Full-text available
Model predictive control provides high performance and safety in the form of constraint satisfaction. These properties however can be satisfied only if the underlying model used for prediction of the controlled process is of sufficient accuracy. One way to address this challenge is by data-driven and machine learning approaches, such as Gaussian pr...
Preprint
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-varying dynamics and behavior of the system. To counteract the changing dynamics, we propose to adapt the parameters of existing controllers to the changing conditions. Doing...
Preprint
Full-text available
Reliable and secure operation of power systems becomes increasingly challenging as the share of volatile generation rises, leading to largely changing dynamics. Typically, the architecture and structure of controllers in power systems, such as voltage controllers of power generators, are fixed during the design and buildup of the network. As replac...
Preprint
Full-text available
Reliable and secure operation of power systems becomes increasingly challenging as the share of volatile generation rises, leading to largely changing dynamics. Typically, the architecture and structure of controllers in power systems, such as voltage controllers of power generators, are fixed during the design and buildup of the network. As replac...