Prof. Dr.-Ing. habil. Ansgar Trächtler - Kontakt (Universität Paderborn)

Autonomous Golf Putting with Data-Driven and Physics-Based Methods

A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: 2022 Sixth IEEE International Conference on Robotic Computing (IRC), IEEE, 2023

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https://arxiv.org/abs/2211.08081

Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design

M. Hesse, M. Hunstig, J. Timmermann, A. Trächtler, in: Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2022, pp. 383-394

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.

Data-Driven Models for Control Engineering Applications Using the Koopman Operator

A. Junker, J. Timmermann, A. Trächtler, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), 2022, pp. 1-9

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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.

https://ieeexplore.ieee.org/document/9836980

Produktkennzeichnung durch lokal definierte Einstellung von ferromagnetischen Eigenschaften beim Drückwalzen von metastabilen Stahlwerkstoffen

B. Arian, W. Homberg, L. Kersting, A. Trächtler, J. Rozo Vasquez, in: 36. Aachener Stahlkolloquium – Umformtechnik “Ideen Form geben“, 2022, pp. 333-347

Autonomes Putten mittels datengetriebener und physikbasierter Methoden

A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022, 2022, pp. 119-124

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https://www.ksp.kit.edu/site/books/e/10.5445/KSP/1000151141/

Learning Data-Driven PCHD Models for Control Engineering Applications

A. Junker, J. Timmermann, A. Trächtler, in: 14th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2022, pp. 389-394

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The design of control engineering applications usually requires a model that accurately represents the dynamics of the real system. In addition to classical physical modeling, powerful data-driven approaches are increasingly used. However, the resulting models are not necessarily in a form that is advantageous for controller design. In the control engineering domain, it is highly beneficial if the system dynamics is given in PCHD form (Port-Controlled Hamiltonian Systems with Dissipation) because globally stable control laws can be easily realized while physical interpretability is guaranteed. In this work, we exploit the advantages of both strategies and present a new framework to obtain nonlinear high accurate system models in a data-driven way that are directly in PCHD form. We demonstrate the success of our method by model-based application on an academic example, as well as experimentally on a test bed.

https://doi.org/10.1016/j.ifacol.2022.07.343

Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments

K. Malena, C. Link, L. Bußemas, S. Gausemeier, A. Trächtler, in: Communications in Computer and Information Science, Springer International Publishing, 2022, pp. 232–254

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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.

Virtual Commissioning of the Trajectory Tracking Control of a Sensor-Guided, Kinematically Redundant Robotic Welding System on a PLC

S. Schütz, R. Schmidt, C. Henke, A. Trächtler, in: 2022 IEEE International Systems Conference (SysCon), IEEE, 2022, pp. 1-8

DOI

Anomaly Detection in Hot Forming Processes using Hybrid Modeling - Part II

C.. Lenz, F. Hanke, C. Henke, A. Trächtler, in: 2022 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2022

DOI

Innovative assistance system for setting up a mechatronic straightening machine

L. Bathelt, F. Bader, E. Djakow, C. Henke, A. Trächtler, W. Homberg, 2022

Abstract

DOI

High-strength wire materials are usually available as strip material which is further processed in a forming process (e.g. punch-bending). For storage and transport of the semi-finished wire to the customer, the material is wound onto coils. The manufacturing and coiling process introduces plastic deformations into the wire, which lead to undesirable residual stresses and wire curvature of the semi-finished product. These residual stresses and curvatures cause variations in the material properties of the semi-finished product, which have a negative impact on the subsequent product quality. Straightening machines are used to compensate the residual stresses and the curvature in the wire. At the beginning of the straightening process, the straightening machines must be set up in such a way that residual stresses and curvatures are optimally compensated. This setup process is usually a manual and iterative process, where a lot of material is wasted until the optimal settings for the straightening machine are found.In order to reduce the amount of material waste, the operator must be supported in the setup process. In this context, a new and innovative setup assistance system was developed to support the operator during the setup process. The setup assistant system automatically detects the wire curvature by means of an optical measuring system. Based on the optically detected measuring points, the wire curvature is determined by a robust calculation algorithm. Based on a database built up through the carried out experimental and numerical research work, the optimum setting parameters for the straightening machine are suggested to the operator without lengthy trial and error. After confirmation by the operator, the roller settings are automatically adjusted by the mechatronic straightening machine. With the presented method, the conventional iterative setup procedure can be made more resource-efficient and a high straightening quality can be reproducibly achieved.

An approach for an innovative 3d steel strip straightening machine for curvature and saber compensation

F. Bader, L. Bathelt, E. Djakow, C. Henke, W. Homberg, A. Trächtler, 2022

Abstract

DOI

Due to increasing globalization and rising quality requirements, the steel and metal processing industry is facing growing cost and innovation pressure. Not least because of their high lightweight potential, high-strength steel materials are meeting the growing material requirements of steel and metal processing in areas such as aerospace and medical technology. In particular, the tight tolerance limits of applicable shape and dimensional accuracies pose a challenge in the processing of high-strength steel strip materials. Improving the processability of high-strength steel materials through the use of straighteners with set-up assistance systems significantly increases the potential for competing with other materials such as aluminum or magnesium alloys.

Mechatronische Richtapparate: Intelligente Richttechnik von hochfesten Flachdrähten

L. Bathelt, F. Bader, E. Djakow, C. Henke, A. Trächtler, W. Homberg, in: Fachtagung VDI MECHATRONIK 2022 , 2022, pp. 19-24

Innovative Online Measurement and Modelling Approach for Property-Controlled Flow Forming Processes

L. Kersting, B. Arian, J.R. Vasquez, A. Trächtler, W. Homberg, F. Walther, Key Engineering Materials (2022), 926, pp. 862-874

Abstract

DOI

<jats:p>The production of complex multi-functional, high-strength parts is becoming increasingly important in the industry. Especially with small batch size, the incremental flow forming processes can be advantageous. The production of parts with complex geometry and locally graded material properties currently depicts a great challenge in the flow forming process. At this point, the usage of closed-loop control for the shape and properties could be a feasible new solution. The overall aim in this project is to establish an intelligent closed-loop control system for the wall thickness as well as the α’-martensite content of AISI 304L-workpieces in a flow forming process. To reach this goal, a novel sensor concept for online measurements of the wall thickness reduction and the martensite content during forming process is proposed. It includes the setup of a modified flow forming machine and the integration of the sensor system in the machine control. Additionally, a simulation model for the flow forming process is presented which describes the forming process with regard to the plastic workpiece deformation, the induced α’-martensite fraction, and the sensor behavior. This model was used for designing a closed-loop process control of the wall thickness reduction that was subsequently realized at the real plant including online measured feedback from the sensor system.</jats:p>

Coupled microscopic and micromagnetic depth-specific analysis of plastic deformation and phase transformation of metastable austenitic steel AISI 304L by flow forming

J. Rozo Vasquez, H. Kanagarajah, B. Arian, L. Kersting, W. Homberg, A. Trächtler, F. Walther, Practical Metallography (2022), 59(11), pp. 660-675

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<jats:title>Abstract</jats:title> <jats:p>This paper presents the characterization of the microstructure evolution during flow forming of austenitic stainless steel AISI 304L. Due to plastic deformation of metastable austenitic steel, phase transformation from γ-austenite into α’-martensite occurs. This is initiated by the formation of shear bands as product of the external stresses. By means of coupled microscopic and micromagnetic investigations, a characterization of the microstructure was carried out. In particular, this study shows the distribution of the strain-induced α’-martensite and its influence on material properties like hardness at different depths. The microstructural analyses by means of electron backscattered diffraction (EBSD) technique, evidence a higher amount of α’-martensite (ca. 23 %) close to the outer specimen surface, where the plastic deformation and the direct contact with the forming tool take place. In the middle area (ca. 1.5 mm depth from the outer surface), the portion of transformed α’-martensite drops to 7 % and in the inner surface to 2 %. These results are well correlated with microhardness and micromagnetic measurements at different depths. EBSD and atomic force microscopy (AFM) were used to make a detailed characterization of the topography and degree of deformation of the shear bands. Likewise, the mechanisms of nucleation of α’-martensite were discussed. This research contributes to the development of micromagnetic sensors to monitor the evolution of properties during flow forming. This makes them more suitable for closed-loop property control, which offers possibilities for an application-oriented and more efficient production.</jats:p>

Tool Wear Monitoring of a Tree Log Bandsaw using a Deep Convolutional Neural Network on challenging data

S. Koppert, C. Henke, A. Trächtler, S. Möhringer, IFAC-PapersOnLine (2022), 55(2), pp. 554-560

DOI

Analysis of Differential Algebraic Equation Systems for Connecting Energy Storages of Generally Valid Functional Mock-up Units

M. Ehlert, C. Henke, A. Trächtler, in: Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SCITEPRESS - Science and Technology Publications, 2022

DOI

A flow forming process model to predict workpiece properties in AISI 304L

B. Arian, A. Oesterwinter, W. Homberg, J. Rozo Vasquez, F. Walther, L. Kersting, A. Trächtler, in: 19th Int. Conference on Metal Forming 2022, 2022

Validation of an Online State Estimation Concept for Microscopic Traffic Simulations◆

K. Malena, C. Link, S. Mertin, S. Gausemeier, A. Trächtler, in: 2021 IEEE Transportation Electrification Conference & Expo (ITEC), IEEE, 2021

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This paper deals with a novel method for the online fitting of a microscopic traffic simulation model to the current state of a real world traffic area. The traffic state estimation is based on limited data of different measurement sources and guarantees general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. The research is embedded in the challenge of improving the traffic by controlling the traffic light systems (TLS) of the examined area. Therefore, the current traffic state and the predicted route choices of individual road users are the matter of interest. The concept is generally transferable to any road traffic system. To give an impression of the accuracy and potential of the approach, the validation and first application results are presented.

Towards the Concept of a Digital Green Twin for a Sustainable Product Lifecycle

J. Michael, E. Grote, S. Pfeifer, R. Rasor, C. Henke, A. Trächtler, L. Kaiser, 2021

Connecting Energy Storages from Tool Independent, Signal-flow Oriented FMUs

M. Ehlert, J. Michael, C. Henke, A. Trächtler, M. Kalla, B. Bagaber, B. Ponick, A. Mertens, in: Proceedings of the International Conference on SMACD and 16th Conference on PRIME, 2021, pp. 164-167

Model of a Triangular Caterpillar Drive and Analysis of Vertical Vehicle Dynamics

V.I. Poddubnyi, A. Trächtler, A. Warkentin, C. Henke, Russian Engineering Research (2021), 41(3), pp. 198-201

Subjective Evaluation of Filter- and Optimization-Based Motion Cueing Algorithms for a Hybrid Kinematics Driving Simulator

P. Biemelt, S. Böhm, S. Gausemeier, A. Trächtler, in: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 1619 - 1626

Kraftsensitive Kalibriermethode für Industrieroboter

S. Schütz, N. Elsner, C. Henke, A. Trächtler, in: Fachtagung VDI MECHATRONIK 2021, 2021

Echtzeitfähige Planung optimierter Trajektorien für sensorgeführte, kinematisch redundante Robotersysteme auf einer Industriesteuerung

S. Schütz, A.T. Rüting, C. Henke, A. Trächtler, at-Automatisierungstechnik (2021), 69(3), pp. 231-241

Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources*

K. Malena, C. Link, S. Mertin, S. Gausemeier, A. Trächtler, in: VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems, SCITEPRESS, 2021, pp. 386-395

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The online fitting of a microscopic traffic simulation model to reconstruct the current state of a real traffic area can be challenging depending on the provided data. This paper presents a novel method based on limited data from sensors positioned at specific locations and guarantees a general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. In these considerations, the actual purpose of research is of particular importance. Here, the research aims at improving the traffic flow by controlling the Traffic Light Systems (TLS) of the examined area which is why the current traffic state and the route choices of individual road users are the matter of interest. An integer optimization problem is derived to fit the current simulation to the latest field measurements. The concept can be transferred to any road traffic network and results in an observation of the current multimodal traffic state matching at the given sensor position. First case studies show promosing results in terms of deviations between reality and simulation.

https://www.scitepress.org/PublicationsDetail.aspx?ID=3xZWfOSENWk=&t=1 - (confirmation)

Forming of metastable austenitic stainless steel tubes with axially graded martensite content by flow-forming

B. Arian, W. Homberg, M. Riepold, A. Trächtler, J. Rozo Vasquez, F. Walther, ULiège Library, 2021

Model approaches for closed-loop property control for flow forming

M. Riepold, B. Arian, J.R. Vasquez, W. Homberg, F. Walther, A. Trächtler, Advances in Industrial and Manufacturing Engineering (2021), 100057

Abstract

DOI

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.

Magnetic Barkhausen noise analysis for microstructural effects separation during flow forming of metastable austenite 304L.

J. Rozo Vasquez, B. Arian, M. Riepold, F. Walther, W. Homberg, A. Trächtler, in: Proceedings of the 11th International Workshop NDT in Progress, 2021

Forming of metastable austenitic stainless steel tubes with axially graded martensite content by flow-forming

B. Arian, W. Homberg, J.R. Vasquez, F. Walther, M. Riepold, A. Trächtler, 24th International Conference on Material Forming, 2021

Model approaches for closed-loop property control for flow forming

M. Riepold, B. Arian, J. Rozo Vasquez, W. Homberg, F. Walther, A. Trächtler, Advances in Industrial and Manufacturing Engineering (2021), 3

Mechanical and mathematical model of a caterpillar drive with a triangular contour for solving problems of vertical dynamics of a tracked vehicle

V.I. Poddubnyi, A. Trächtler, A. Warkentin, C. Henke, Vestnik Mashinostroeniya (2020), pp. 26-29

DOI

Design and Objective Evaluation of Filter- and Optimization-based Motion Cueing Strategies for a Hybrid Kinematics Driving Simulator with 5 Degrees of Freedom

P. Biemelt, S. Gausemeier, A. Trächtler, International Journal On Advances in Systems and Measurements (2020), 13(3 & 4), pp. 203-219

Design and Evaluation of a Novel Filter-Based Motion Cueing Strategy for a Hybrid Kinematics Driving Simulator with 5 Degrees of Freedom

P. Biemelt, S. Mertin, N. Rüddenklau, S. Gausemeier, A. Trächtler, in: Proceedings of the Driving Simulation Conference Europe VR, Driving Simulation Association, 2020, pp. 85-92

Macroscopic Traffic Flow Control using Consensus Algorithms

S. Mertin, K. Malena, C. Link, S. Gausemeier, A. Trächtler, in: The 23rd IEEE International Conference on Intelligent Transportation Systems, International Conference on Intelligent Transportation Systems (ITSC), 2020

Real-Time Optimized Model Predictive Control of an Active Roll Stabilization System with Actuator Limitations

G. Nareyko, P. Biemelt, A. Trächtler, in: Proceedings of the 21st IFAC World Congress, IFAC, 2020

A Model-Based Online Reference Prediction Strategy for Model Predictive Motion Cueing Algorithms

P. Biemelt, C. Link, S. Gausemeier, A. Trächtler, in: Proceedings of the 21st IFAC World Congress, 2020, pp. 6082 - 6088

Echtzeitfähige Planung optimierter Trajektorien für sensorgeführte, kinematisch redundante Mechanismen auf einer Industriesteuerung

S. Schütz, A.T. Rüting, C. Henke, A. Trächtler, in: Entwurf komplexer Automatisierungssysteme (EKA), 2020

Microstructural investigation on phase transformation during flow forming of the metastable austenite AISI 304

J. Rozo Vasquez, B. Arian, M. Riepold, W. Homberg, A. Trächtler, F. Walther, in: 54. Metallographie-Tagung, 2020, pp. 75-81

Objective Evaluation of a Novel Filter-Based Motion Cueing Algorithm in Comparison to Optimization-Based Control in Interactive Driving Simulation

P. Biemelt, S. Mertin, N. Rüddenklau, S. Gausemeier, A. Trächtler, in: Proceedings of the International Conference on Advances in System Simulation (SIMUL), IARIA, 2019

Simulation-Based Lighting Function Development of High-Definition Headlamps

N. Rüddenklau, P. Biemelt, S. Mertin, S. Gausemeier, A. Trächtler, in: 13th International Symposium on Automotive Lighting (ISAL), utzverlag GmbH, 2019, pp. 677-686

Proof-of-Concept einer komplexen Co-Simulationsumgebung für einen Fahrsimulator zur Untersuchung von Car2X-Kommunikations-Szenarien

S. Mertin, D. Buse, M. Franke, A. Trächtler, S. Gausemeier, F. Dressler, in: VDI/VDE AUTOREG 2019, VDI Verlag Düsseldorf, 2019, pp. 159-170

Hardware-in-the-Loop Simulation of High-Definition Headlamp Systems

N. Rüddenklau, S. Gausemeier, A. Trächtler, in: VDI/VDE AUTOREG 2019, VDI- Verlag, Düsseldorf, 2019

Model Predictive Control of an Active Roll Stabilization System

G. Nareyko, T. Koch, A. Trächtler, in: VDI/VDE Fachtagung AUTOREG, 2019

Assisted setup of forming processes: architecture for the integration of non-adjustable disturbances

M. Gräler, A. Wallow, C. Henke, A. Trächtler, Procedia CIRP (2019), 81, pp. 1348–1353

Decentralized Energy Management for Smart Home System of Systems

J. Michael, C. Henke, A. Trächtler, in: Syscon 2019 - The 13th Annual IEEE International Systems Conference, IEEE SYSCON, 2019, pp. 524-531

Regelung kollaborativer Robotersysteme zur benutzerfreundlichen, flexiblen Fertigung kleiner Losgrößen am Beispiel eines halbautomatischen Schweißvorgangs

S. Schütz, A.T. Rüting, C. Henke, A. Trächtler, in: Fachtagung Mechatronik 2019, VDI Mechatronik, 2019, pp. 43-48

Open-loop linearization for piezoelectric actuator with inverse hysteresis model

M. Riepold, S. Maslo, G. Han, C. Henke, A. Trächtler, Vibroengineering PROCEDIA (2019), 22, pp. 47-52

Umsetzung einer echtzeitfähigen modellprädiktiven Trajektorienplanung für eine mehrachsige Hybridkinematik auf einer Industriesteuerung

A.T. Rüting, C. Henke, A. Trächtler, at-Automatisierungstechnik (2019), 67(4), pp. 326–336

Hardware-in-the-Loop Simulation for a Multiaxial Suspension Test Rig with a Nonlinear Spatial Vehicle Dynamics Model

P. Traphöner, S. Olma, A. Kohlstedt, N. Fast, K. Jäker, A. Trächtler, in: 8th IFAC Symposium on Mechatronic Systems, 2019

Hardware-in-the-Loop-Simulation einer Fahrzeugachse mit aktiver Wankstabilisierung mithilfe eines hydraulischen Hexapoden

P. Traphöner, A. Kohlstedt, S. Olma, K. Jäker, A. Trächtler, in: 13. VDI/VDE Mechatronik-Tagung, VDI-Verlag, 2019, pp. 85-90

Real-Time Lighting of High-Definition Headlamps for Night Driving Simulation

N. Rüddenklau, P. Biemelt, S. Mertin, S. Gausemeier, A. Trächtler, in: IARIA SysMea, IARIA, 2019, pp. 72-88

Steigerung der Intelligenz mechatronischer Systeme

J. Gausemeier, A. Trächtler, Springer-Verlag GmbH, 2018

Trainingsgerät mit Laufbandeinheit

J. Tominski, D. Zimmer, V. Just, C. Lankeit, F. Oestersötebier, A. Trächtler. Trainingsgerät mit Laufbandeinheit, Patent DE 10 2017 003 587 A1. 2018.

Intelligente Steuerungen und Regelungen

C. Lüke, J. Timmermann, J.H. Kessler, A. Trächtler, in: Steigerung der Intelligenz mechatronischer Systeme, Springer Vieweg, 2018, pp. 153-192

Ressourceneffiziente Selbstoptimierende Wäscherei

A. Trächtler, Springer, 2018

Shader-Based Realtime Simulation of High-Definition Automotive Headlamps

N. Rüddenklau, P. Biemelt, S. Henning, S. Gausemeier, A. Trächtler, in: SIMUL 2018, The Tenth International Conference on Advances in System Simulation, IARIA, 2018

Rotordynamic instabilities in washing machines

S. Drüke, R. Bicker, B. Schullter, C. Henke, A. Trächtler, in: Proceedings of the 10th International Conference on Rotor Dynamics - IFToMM. Vol. 2. International Conference on Rotor Dynamics - IFToMM, Springer Nature Switzerland AG, 2018, pp. 383-397

Assisted setup of forming processes: compensation of initial stochastic disturbances

M. Gräler, R. Springer, C. Henke, A. Trächtler, W. Homberg, Swedish Production Symposium (2018), 25, pp. 358-364

Umsetzung einer echtzeitfähigen Mehrgrößenoptimierung auf einer Industriesteuerung

A.T. Rüting, C. Henke, A. Trächtler, in: EKA 2018 Entwurf komplexer Automatisierungssysteme - Beschreibungsmittel, Methoden, Werkzeuge und Anwendungen, IFAK - Institut für Automation und Kommunikation e.V., 2018

Model based Setup Assistant for Progressive Tools

R. Springer, M. Graeler, W. Homberg, C. Henke, A. Trächtler, AIP Conference Proceedings (2018), 160025(2018)

A Simulation Framework for Testing a Conceptual Hierarchical Autonomous Traffic Management System including an Intelligent External Traffic Simulation

S. Henning, P. Biemelt, N. Rüddenklau, S. Gausemeier, A. Trächtler, in: Proceedings of the DSC 2018 Europe VR: New trends in Human in the Loop simulation and testing. Driving simulation and VR, Driving Simulation Association, 2018, pp. 91-98

Holistic Requirements for Interdisciplinary Development Processes

C. Lankeit, J. Michael, C. Henke, A. Trächtler, in: Proceedings 1st International Workshop on Learning from other Disciplines for Requirements Engineering, IEEE, 2018, pp. 4-7

Model-based precision position and force control of SMA actuators with a clamping application

A. Pai, M. Riepold, A. Trächtler, Mechatronics (2018), 50, pp. 303-320

Observer-based nonlinear control strategies for Hardware-in-the-Loop simulations of multiaxial suspension test rigs

S. Olma, A. Kohlstedt, P. Traphöner, K. Jäker, A. Trächtler, Mechatronics (2018), 50, pp. 212-224

Rapid-Control-Prototyping as part of Model-Based Development of Heat Pump Dryers

J. Holtkötter, J. Michael, C. Henke, A. Trächtler, M. Bockholt, A. Möhlenkamp, M. Katter, Procedia Manufacturing (2018), 24, pp. 235–242

A Model Predictive Motion Cueing Strategy for a 5-Degree-of-Freedom Driving Simulator with Hybrid Kinematics

P. Biemelt, S. Henning, N. Rüddenklau, S. Gausemeier, A. Trächtler, in: Proceedings of the Driving Simulation Conference Europe VR (DSC), 2018, pp. 79-85

A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart

M. Hesse, J. Timmermann, E. Hüllermeier, A. Trächtler, Procedia Manufacturing (2018), 24, pp. 15 - 20

Abstract

The effective control design of a dynamical system traditionally relies on a high level of system understanding, usually expressed in terms of an exact physical model. In contrast to this, reinforcement learning adopts a data-driven approach and constructs an optimal control strategy by interacting with the underlying system. To keep the wear of real-world systems as low as possible, the learning process should be short. In our research, we used the state-of-the-art reinforcement learning method PILCO to design a feedback control strategy for the swing-up of the double pendulum on a cart with remarkably few test iterations at the test bench. PILCO stands for “probabilistic inference for learning control” and requires only few expert knowledge for learning. To achieve the swing-up of a double pendulum on a cart to its upper unstable equilibrium position, we introduce additional state restrictions to PILCO, so that the limited cart distance can be taken into account. Thanks to these measures, we were able to learn the swing up at the real test bench for the first time and in only 27 learning iterations.

Intelligente technische Systeme

E. Bodden, F. Dressler, F. Meyer auf der Heide, C. Scheytt, A. Trächtler, Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2017

Wissenschaftsforum Intelligente Technische Systeme (WInTeSys)

J. Gausemeier, E. Bodden, F. Dressler, R. Dumitrescu, F. Meyer auf der Heide, C. Scheytt, A. Trächtler, Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2017

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Das Wissenschaftsforum Intelligente Technische Systeme (WInTeSys) legt am 11. und 12. Mai 2017 in Paderborn den Schwerpunkt auf die Grundlagen und die Entwicklung intelligenter technischer Systeme im Kontext Industrie 4.0. Etwa 40 begutachtete hochkarätige Beiträge geben einen Überblick über Forschungsfelder, Technologien und Anwendungen. Die Veranstaltung bietet den Teilnehmerinnen und Teilnehmern eine ausgezeichnete Bühne für den Erfahrungsaustausch auf dem Weg in die Digitalisierung von Produkten und Produktionssystemen. »Das Besondere ist der Dialog von Hochschulforschung und industrieller Entwicklung, also das Aufeinandertreffen von »Science-Push« und »Application-Pull«. Die Beiträge spiegeln die hervorragende Vernetzung in der Region OWL und darüber hinaus wider«, sagt Veranstalter Prof. Jürgen Gausemeier (Heinz Nixdorf Institut, Universität Paderborn).

https://digital.ub.uni-paderborn.de/hs/content/titleinfo/2436759 - (confirmation)

An Application-Oriented Design Method for Networked Driving Simulation

K. Abdelgawad, J. Gausemeier, A. Trächtler, S. Gausemeier, R. Dumitrescu, J. Berssenbrügge, J. Stöcklein, M. Grafe, Designs ‒ International Journal of Engineering Designs, Band 1 (2017), 1, pp. 6.1-6.47

Wissenschaftsforum Intelligente Technische Systeme (WInTeSys). , Band 369

J. Gausemeier, E. Bodden, F. Dressler, R. Dumitrescu, F. Meyer auf der Heide, C. Scheytt, A. Trächtler. Wissenschaftsforum Intelligente Technische Systeme (WInTeSys). , Band 369. 2017.

Immer besser: Maschinen optimieren sich selbst

A. Trächtler, P. Iwanek, G. Scheffels, Konstruktion (2017)

Virtuelle Inbetriebnahme eines Fertigungszentrums

C. Henke, J. Michael, C. Lankeit, A. Trächtler, in: Tag des System Engineering, Gesellschaft für Systems Engineering e.V., 2017, pp. 45-54

Modellbasierte Untersuchung der Zuverlässigkeit algorithmisch bestimmter kritischer Stellen in Straßennetzwerken

S. Henning, P. Biemelt, K. Abdelgawad, S. Gausemeier, A. Trächtler, in: VDI/VDE (AUTOREG 2017), VDI-Verlag, Düsseldorf, 2017

Fast hybrid position / force control of a parallel kinematic load simulator for 6-DOF Hardware-in-the-Loop axle tests

A. Kohlstedt, P. Traphöner, S. Olma, K. Jäker, A. Trächtler, in: 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, 2017, pp. 694–699

Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control

K. Xu, J. Timmermann, A. Trächtler, in: Proc. Advanced Intelligent Mechatronics (AIM), IEEE, 2017

Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees

K. Xu, J. Timmermann, A. Trächtler, in: Proc. 20th IFAC World Congress, 2017

Networked Driving Simulation for Future Autonomous and Cooperative Vehicle Systems

K. Abdelgawad, S. Henning, P. Biemelt, S. Gausemeier, A. Trächtler, in: VDI/VDE (AUTOREG 2017), VDI-Verlag, Düsseldorf, 2017

Model-Based Design of Self-Correcting Forming Processes

M. Krüger, M. Borsig, U. Damerow, M. Gräler, A. Trächtler, in: Math for the Digital Factory, Springer International Publishing, 2017, pp. 273-288

Universelle Entwicklungs- und Prüfumgebung für mechatronische Fahrzeugachsen

P. Traphöner, S. Olma, A. Kohlstedt, K. Jäker, A. Trächtler, in: Wissenschaftsforum Intelligente Technische Systeme (WInTeSys) 2017, Heinz Nixdorf Institut, 2017

Wissenschaftsforum Intelligente Technische Systeme (WInTeSys)

J. Gausemeier, E. Bodden, F. Dressler, R. Dumitrescu, F. Meyer auf der Heide, C. Scheytt, A. Trächtler, Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2017

A Holistic Approach for Virtual Commissioning of Intelligent Systems

C. Henke, J. Michael, C. Lankeit, A. Trächtler, in: Systems Conference 2017, IEEE, 2017

Dynamische Prozessplanung im Smart Home auf Basis von Mutliagentensystemen

J. Michael, A. Hellweg, C. Henke, A. Trächtler, in: Fachtagung Mechatronik 2017, VDI Mechatronik, 2017, pp. 18-23

Kinematics-based force/position control of a hexapod in a HiL axle test rig

A. Kohlstedt, S. Olma, P. Traphöner, K. Jäker, A. Trächtler, in: 17. Internationales Stuttgarter Symposium, Band 2, Springer, 2017, pp. 379-392

Modellprädiktive Vorsteuerung für einen kinematisch redundanten hybridkinematischen Mechanismus im Industrieumfeld

A.T. Rüting, E. Block, A. Trächtler, in: Fachtagung Mechatronik 2017, VDI Mechatronik, 2017, pp. 250-255

Innovative Suspensions for Caterpillar Vehicles

W. Poddubny, A. Trächtler, A.P. Warkentin, M. Krüger, Russian Engineering Research (2017), 37(6), pp. 485–489

Methodology for Determining Critical Locations in Road Networks based on Graph Theory

S. Henning, P. Biemelt, K. Abdelgawad, S. Gausemeier, A. Trächtler, in: IFAC World Congress 2017, IFAC, 2017

Scientifc Automation: Hochpräzise Analysen direkt in der Steuerung

J. Papenfort, F. Bause, U. Frank, S. Strughold, A. Trächtler, D. Bielawny, C. Henke, in: Wissenschaftsforum Intelligente Technische Systeme (WinTeSys) , Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn, 2017

Reliable Multipath Communication Approach for Internet-based Cyber-physical Systems

M. Elattar, J. Jasperneite, A. Trächtler, a. et, in: 26th IEEE International Symposium on Industrial Electronics (ISIE), 2017

Mechanisch - mathematisches Modell eines Kettenfahrzeuges für die Entwicklung innovativer Antriebs- und Federungssysteme (auf russ.)

W. Poddubny, A. Trächtler, A.P. Warkentin, M. Krüger, Interbranch Scientific and Technical Magazine «Vestnik Mashinostroeniya» (2017)

Visualization of Headlight Illumination for the Virtual Prototyping of Light-Based Driver Assistance Systems

J. Berssenbrügge, A. Trächtler, C. Schmidt, Journal of Computing and Information Science in Engineering, Band 16(3) (2016)

Advanced Traffic Simulation Framework for Networked Driving Simulators

K. Abdelgawad, S. Henning, P. Biemelt, S. Gausemeier, A. Trächtler, in: AAC2016 (IFAC), 8th IFAC Symposium on Advances in Automotive Control (AAC 2016), IFAC, 2016

Indirect Force Control in Hardware-in-the-Loop Simulations for a Vehicle Axle Test Rig

S. Olma, A. Kohlstedt, P. Traphöner, K. Jäker, A. Trächtler, in: 14th International Conference on Control, Automation Robotics & Vision (ICARCV), IEEE, 2016

Feature Model Based Interface Design for Development of Mechatronic Systems

F. Abrishamchian, A. Trächtler, IEEE, 2016

PROFINET-Implementierung im Rahmen der Entwicklung eines intelligenten, selbstlernenden Teigkneters

J. Holtkötter, J. Michael, C. Henke, A. Trächtler, F. Oestersötebier, S. Wessels, in: Virtuelle Instrumente in der Praxis 2016, VDE Verlag, 2016

A HRRN based scheduling for FMS and RMS with networked control and product-intelligence

F. Bertelsmeier, J. Pollmann, A. Trächtler, in: Inproceedings of the IEEE IECON 2016, IEEE, 2016

Model Predictive Feedforward Compensation for Control of Multi Axes Hybrid Kinematics on PLC

A.T. Rüting, L.M. Blumenthal, A. Trächtler, in: Proceedings of IEEE IECON 2016, IEEE, 2016

Nichtlineare Zustandsregelung mit Sliding-Mode-Beobachter für einen Achsprüfstand mit hydraulischem Hexapoden

S. Olma, P. Traphöner, A. Kohlstedt, A. Trächtler, in: GMA Fachausschuss 1.40 "Theoretische Verfahren der Regelungstechnik", VDI/VDE-GMA, 2016

Substructuring and Control Strategies for Hardware-in-the-Loop Simulations of Multiaxial Suspension Test Rigs

S. Olma, A. Kohlstedt, P. Traphöner, K. Jäker, A. Trächtler, in: Proceedings of the 7th IFAC Symposium on Mechatronic Systems, IFAC, 2016

Study of a rail-bound parallel robot concept with curvilinear closed-path tracks

D. Bielawny, P. Wang, A. Trächtler, in: Proceedings of 7th IFAC Symposium on Mechatronic Systems & 15th Mechatronics Forum International Conference, 2016