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Photo: LDM

Photo: LDM

Photo: @AdobeStock/Gorodenkoff

Photo: © AdobeStock/Gorodenkoff

Photo: @ Fraunhofer IOSB-INA

Photo: © AdobeStock/Gorodenkoff

Photo: @ Fraunhofer IEM

Photo: @ Heinz Nixdorf Institut

Photo: @ Heinz Nixdorf Institut

Photo: @ Heinz Nixdorf Institut

Photo: @AdobeStock/Gorodenkoff

Prof. Dr.-Ing. habil. Ansgar Trächtler

Contact
Publications

Heinz Nixdorf Institut

Committee - Professor

Faculty of Mechanical Engineering

Section Owner - Professor

Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM

Head of Institute - Professor

Phone:
+49 5251 60-6277
Fax:
+49 5251 60-6297
Office:
F0.328
Web:
Visitor:
Fürstenallee 11
33102 Paderborn

Open list in Research Information System

2022

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

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

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.


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


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

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.


2021

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

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.


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

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


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

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.


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

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


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


2020

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


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

J. Rozo Vasquez, A. Bahman, M. Riepold, W. Homberg, A. Trächtler, F. Walther, in: Fortschritte in der Metallographie, Saarbruecken Germany, 2020, pp. 75-81


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


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

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


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


2019

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


2018

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



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

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.


2017

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

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).


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)


2016

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



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


Multiobjective Model Predictive Control of an Industrial Laundry

S. Peitz, M. Graeler, C. Henke, M. Hessel-von Molo, M. Dellnitz, A. Trächtler, 3rd International Conference on System-integrated Intelligence: New Challenges for Product and Production Engineering (2016), Procedia Technology 26 , pp. 483 – 490


Design and Implementation of Intelligent Control Software for a Dough Kneader

F. Oestersötebier, P. Traphöner, F. Reinhart, S. Wessels, A. Trächtler, in: Proceedings of the 3rd International Conference on System-Integrated Intelligence, Elsevier, 2016


A Novel Approach Using Model Predictive Control to Enhance the Range of Electric Vehicles

J. Eckstein, C. Lüke, F. Brunstein, P. Friedel, U. Köhler, A. Trächtler, in: 3rd International Conference on System-integrated Intelligence: New Challenges for Product and Production Engineering, SysInt 2016, 2016


Approach for an Integrated Model-Based System Design of Intelligent Dynamic Systems Using Solution and System Knowledge

F. Oestersötebier, F. Abrishamchian, C. Lankeit, V. Just, A. Trächtler, in: Proceedings of the 3rd International Conference on System-Integrated Intelligence, Elsevier, 2016


Model-based method for the accuracy analysis of Hardware-in-the-Loop test rigs for mechatronic vehicle axles

S. Olma, P. Traphöner, A. Kohlstedt, K. Jäker, A. Trächtler, in: Proceedings of the 3rd International Conference on System-Integrated Intelligence, Elsevier, 2016


Disturbance Observer Design for Utilizing of Time-delayed Vision Measurements in High Dynamic Systems

S. Wang, V. Just, A. Trächtler, in: Proceedings of the 3rd International Conference on System-Integrated Intelligence, Elsevier, 2016


Development and design of intelligent product carriers for flexible networked control of distributed manufacturing processes

F. Bertelsmeier, S. Schöne, A. Trächtler, in: Inproceedings of the IEEE 24th Mediterranean Confernce on Control and Automation (MED), IEEE, 2016


Control of a hydraulic hexapod for a Hardware-in-the-Loop axle test rig

A. Kohlstedt, S. Olma, S. Flottmeier, P. Traphöner, K. Jäker, A. Trächtler, at-Automatisierungstechnik (2016), 64(5), pp. 365-374


Consistency Analysis for Requirements, Functions, and System Elements

C. Lankeit, V. Just, A. Trächtler, in: IEEE Systems Conference, 2016


Modellbildung und Simulation im Kontext des Systems Engineering

J. Michael, J. Holtkötter, C. Henke, A. Trächtler, in: ASIM-Treffen STS/GMMS 2016, 2016, pp. 174-179


Implementing intelligent technical systems into smart homes by using model based systems engineering and multi-agent systems

J. Michael, M. Hillebrand, B. Wohlers, C. Henke, R. Dumitrescu, A. Trächtler, Renewable Energy and Power Quality Journal (RE&PQJ) 16 (2016), 1(14), pp. 359-364


Precision Control of SMA Actuators with a Real Time Model-Based Controller and Extended VSC

A. Pai, M. Riepold, A. Trächtler, IFAC-PapersOnLine (2016), 49(21), pp. 66–73


Regelungstechnik

O. Föllinger, U. Konigorski, B. Lohmann, G. Roppenecker, A. Trächtler, VDE-Verlag, 2016


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