@book{47417, author = {{Föllinger, Otto and Konigorski, Ulrich and Lohmann, Boris and Roppenecker, Günter and Trächtler, Ansgar}}, publisher = {{VDE-Verlag}}, title = {{{Regelungstechnik. Einführung in die Methoden und ihre Anwendung}}}, year = {{2022}}, } @inproceedings{26539, abstract = {{In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.}}, author = {{Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)}}, keywords = {{data-driven, physics-based, physics-informed, neural networks, system identification, hybrid modelling}}, location = {{Cairo, Egypt}}, pages = {{67--76}}, title = {{{Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering}}}, doi = {{10.1109/AIRC56195.2022.9836982}}, year = {{2022}}, } @inproceedings{31066, abstract = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }}, author = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}}, keywords = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}}, location = {{Casablanca, Morocco}}, number = {{12}}, pages = {{19--24}}, title = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}}, doi = {{https://doi.org/10.1016/j.ifacol.2022.07.282}}, volume = {{55}}, year = {{2022}}, } @inproceedings{29803, abstract = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of bonding: the constraint is used to check one quality feature of the process and the use of batches leads to more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding process that provides very high quality bonds without using a physical model. We also show that the quality of the Bayesian optimization based control outperforms random search as well as manual search by a user. Using a simple prior knowledge model derived from data further improves the quality of the connection. The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary, Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward control without full modeling of the underlying physical processes.}}, author = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}}, isbn = {{978-989-758-549-4}}, keywords = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}}, location = {{Online}}, pages = {{383--394}}, title = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}}, year = {{2022}}, } @inbook{33849, abstract = {{Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed.}}, author = {{Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Communications in Computer and Information Science}}, editor = {{Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}}, isbn = {{9783031170973}}, issn = {{1865-0929}}, keywords = {{Traffic control, Traffic estimation, Real-time, MPC, Fuzzy, Isolated intersection, Networked intersection, Sensor fusion}}, pages = {{232–254}}, publisher = {{Springer International Publishing}}, title = {{{Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}}}, doi = {{10.1007/978-3-031-17098-0_12}}, volume = {{1612}}, year = {{2022}}, } @inproceedings{26389, abstract = {{Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.}}, author = {{Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}}, booktitle = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)}}, isbn = {{978-1-6654-5946-4}}, keywords = {{Koopman Operator, Nonlinear Control, Extended Dynamic Mode Decomposition, Hybrid Modelling}}, location = {{Cairo, Egypt}}, pages = {{1--9}}, title = {{{Data-Driven Models for Control Engineering Applications Using the Koopman Operator}}}, doi = {{10.1109/AIRC56195.2022.9836980}}, year = {{2022}}, } @inproceedings{34011, author = {{Junker, Annika and Fittkau, Niklas and Timmermann, Julia and Trächtler, Ansgar}}, booktitle = {{Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022}}, location = {{Berlin, Germany}}, pages = {{119--124}}, title = {{{Autonomes Putten mittels datengetriebener und physikbasierter Methoden}}}, doi = {{10.5445/KSP/1000151141}}, year = {{2022}}, } @article{50071, author = {{Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}}, issn = {{2405-8963}}, journal = {{IFAC-PapersOnLine}}, keywords = {{Control and Systems Engineering}}, number = {{12}}, pages = {{389--394}}, publisher = {{Elsevier BV}}, title = {{{Learning Data-Driven PCHD Models for Control Engineering Applications*}}}, doi = {{10.1016/j.ifacol.2022.07.343}}, volume = {{55}}, year = {{2022}}, } @inproceedings{26180, author = {{Michael, Jan and Grote, Eva-Maria and Pfeifer, Stefan and Rasor, Rik and Henke, Christian and Trächtler, Ansgar and Kaiser, Lydia}}, title = {{{Towards the Concept of a Digital Green Twin for a Sustainable Product Lifecycle}}}, year = {{2021}}, } @inproceedings{26997, author = {{Ehlert, Meik and Michael, Jan and Henke, Christian and Trächtler, Ansgar and Kalla, Matthias and Bagaber, Bakr and Ponick, Bernd and Mertens, Axel}}, booktitle = {{Proceedings of the International Conference on SMACD and 16th Conference on PRIME}}, pages = {{164--167}}, title = {{{Connecting Energy Storages from Tool Independent, Signal-flow Oriented FMUs}}}, year = {{2021}}, } @article{27123, author = {{Poddubnyi, Vladimir I. and Trächtler, Ansgar and Warkentin, Andreas and Henke, Christian}}, journal = {{Russian Engineering Research}}, number = {{3}}, pages = {{198--201}}, publisher = {{Springer}}, title = {{{Model of a Triangular Caterpillar Drive and Analysis of Vertical Vehicle Dynamics}}}, volume = {{41}}, year = {{2021}}, } @inproceedings{23576, author = {{Biemelt, Patrick and Böhm, Sabrina and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC)}}, location = {{Melbourne, Australia}}, pages = {{1619 -- 1626}}, title = {{{Subjective Evaluation of Filter- and Optimization-Based Motion Cueing Algorithms for a Hybrid Kinematics Driving Simulator}}}, year = {{2021}}, } @inproceedings{22961, author = {{Schütz, Stefan and Elsner, Nikolaus and Henke, Christian and Trächtler, Ansgar}}, booktitle = {{Fachtagung VDI MECHATRONIK 2021}}, title = {{{Kraftsensitive Kalibriermethode für Industrieroboter}}}, year = {{2021}}, } @article{22962, author = {{Schütz, Stefan and Rüting, Arne Thorsten and Henke, Christian and Trächtler, Ansgar}}, journal = {{at-Automatisierungstechnik}}, number = {{3}}, pages = {{231--241}}, title = {{{Echtzeitfähige Planung optimierter Trajektorien für sensorgeführte, kinematisch redundante Robotersysteme auf einer Industriesteuerung}}}, volume = {{69}}, year = {{2021}}, } @article{23469, abstract = {{The implementation of control systems in metal forming processes improves product quality and productivity. By controlling workpiece properties during the process, beneficial effects caused by forming can be exploited and integrated in the product design. The overall goal of this investigation is to produce tailored tubular parts with a defined locally graded microstructure by means of reverse flow forming. For this purpose, the proposed system aims to control both the desired geometry of the workpiece and additionally the formation of strain-induced α′-martensite content in the metastable austenitic stainless steel AISI 304 L. The paper introduces an overall control scheme, a geometry model for describing the process and changes in the dimensions of the workpiece, as well as a material model for the process-induced formation of martensite, providing equations based on empirical data. Moreover, measurement systems providing a closed feedback loop are presented, including a novel softsensor for in-situ measurements of the martensite content.}}, author = {{Riepold, Markus and Arian, Bahman and Vasquez, Julian Rozo and Homberg, Werner and Walther, Frank and Trächtler, Ansgar}}, issn = {{2666-9129}}, journal = {{Advances in Industrial and Manufacturing Engineering}}, title = {{{Model approaches for closed-loop property control for flow forming}}}, doi = {{10.1016/j.aime.2021.100057}}, year = {{2021}}, } @phdthesis{42070, author = {{Olma, Simon}}, isbn = {{9783947647231}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts}}, title = {{{Systemtheorie von Hardware-in-the-Loop-Simulationen mit Anwendung auf einem Fahrzeugachsprüfstand mit parallelkinematischem Lastsimulator}}}, volume = {{404}}, year = {{2021}}, } @phdthesis{42069, author = {{Lankeit, Christopher}}, title = {{{Systematik zur Evolution technischer Anforderungen}}}, year = {{2021}}, } @phdthesis{28370, author = {{Kohlstedt, Andreas}}, isbn = {{978-3-947647-15-6}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Modellbasierte Synthese einer hybriden Kraft-/Positionsregelung für einen Fahrzeugachsprüfstand mit hydraulischem Hexapod}}}, volume = {{396}}, year = {{2021}}, } @inproceedings{30297, author = {{Rozo Vasquez, Julian and Arian, Bahman and Riepold, Markus and Walther, Frank and Homberg, Werner and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 11th International Work­shop NDT in Progress}}, location = {{Prague}}, title = {{{Magnetic Barkhausen noise analysis for microstructural effects separation during flow forming of metastable austenite 304L.}}}, year = {{2021}}, } @inproceedings{23465, abstract = {{One of the main objectives of production engineering is to reproducibly manufacture (complex) defect-free parts. To achieve this, it is necessary to employ an appropriate process or tool design. While this will generally prove successful, it cannot, however, offset stochastic defects with local variations in material properties. Closed-loop process control represents a promising approach for a solution in this context. The state of the art involves using this approach to control geometric parameters such as a length. So far, no research or applications have been conducted with closed-loop control for microstructure and product properties. In the project on which this paper is based, the local martensite content of parts is to be adjusted in a highly precise and reproducible manner. The forming process employed is a special, property-controlled flow-forming process. A model-based controller is thus to generate corresponding correction values for the tool-path geometry and tool-path velocity on the basis of online martensite content measurements. For the controller model, it is planned to use a special process or microstructure (correlation) model. The planned paper not only describes the experimental setup but also presents results of initial experimental investigations for subsequent use in the closed-loop control of α’-martensite content during flow-forming.}}, author = {{Arian, Bahman and Homberg, Werner and Riepold, Markus and Trächtler, Ansgar and Rozo Vasquez, Julian and Walther, Frank}}, isbn = {{978-2-87019-302-0}}, keywords = {{Flow-forming, Spinning, Process Strategy, Martensite Content, Property Control, Micromagnetic Measurement, Metastable Austenitic Stainless Steel}}, location = {{Liège, Belgium}}, publisher = {{ULiège Library}}, title = {{{Forming of metastable austenitic stainless steel tubes with axially graded martensite content by flow-forming}}}, year = {{2021}}, }