@inproceedings{ZindlerKorn1996, author = {Zindler, Klaus and Korn, Ulrich}, title = {Vollst{\"a}ndige Modale Synthese robuster Regelungen mittels Optimierung eines H∞-Kostenfunktionals}, series = {30. Regelungstechnisches Kolloquium, Boppard, Februar 1996}, volume = {1996}, booktitle = {30. Regelungstechnisches Kolloquium, Boppard, Februar 1996}, year = {1996}, subject = {Robuste Regelung}, language = {de} } @inproceedings{Zindler2008, author = {Zindler, Klaus}, title = {Federn, D{\"a}mpfen, Stabilisieren - Beispiele f{\"u}r den Einsatz moderner Regelungstechnik in der Mechatronik}, series = {Forum f{\"u}r Forschung und Wirtschaft der Industrievereinigung Odenwaldkreis, Michelstadt, Januar 2008}, volume = {2008}, booktitle = {Forum f{\"u}r Forschung und Wirtschaft der Industrievereinigung Odenwaldkreis, Michelstadt, Januar 2008}, year = {2008}, subject = {Regelungstechnik}, language = {de} } @misc{Zindler2009, author = {Zindler, Klaus}, title = {"Federn, D{\"a}mpfen, Stabilisieren" - Moderne Regelungstechnik in der Mechatronik}, series = {Vortragsreihe "Verstandene Technik Begeistert"}, volume = {2009}, journal = {Vortragsreihe "Verstandene Technik Begeistert"}, address = {Aschaffenburg}, year = {2009}, subject = {Regelungstechnik}, language = {de} } @inproceedings{Zindler2002, author = {Zindler, Klaus}, title = {Erzielbare Komfortverbesserung mit aktiven Federungen unter Verwendung einer Sky-Hook-Regelstrategie}, series = {Vortrag im Forschungs- und Innovationszentrum der BMW AG, M{\"u}nchen, Juni 2002}, volume = {2002}, booktitle = {Vortrag im Forschungs- und Innovationszentrum der BMW AG, M{\"u}nchen, Juni 2002}, year = {2002}, subject = {Kraftfahrzeugfederung}, language = {de} } @misc{Zindler2002, author = {Zindler, Klaus}, title = {Das mechatronische Fahrwerkkonzept}, series = {Vortrag im Forschungs- und Innovationszentrum der BMW AG}, volume = {2002}, journal = {Vortrag im Forschungs- und Innovationszentrum der BMW AG}, number = {Januar}, address = {M{\"u}nchen}, year = {2002}, subject = {Kraftfahrzeug}, language = {de} } @inproceedings{Zindler1997, author = {Zindler, Klaus}, title = {Vollst{\"a}ndige Modale Synthese robuster Regelungen unter Verwendung eines H∞-Kostenfunktionals}, series = {Kolloquium des Deutschen Zentrums f{\"u}r Luft- und Raumfahrt (DLR), Oberpfaffenhofen, April 1997}, volume = {1997}, booktitle = {Kolloquium des Deutschen Zentrums f{\"u}r Luft- und Raumfahrt (DLR), Oberpfaffenhofen, April 1997}, year = {1997}, subject = {Robuste Regelung}, language = {de} } @inproceedings{Zindler1999, author = {Zindler, Klaus}, title = {Eine Strategie zur Erlangung robuster Stabilit{\"a}t unter Nutzung der Freiheitsgrade beim Entwurf f{\"u}r Mehrgr{\"o}ßensysteme}, series = {Kolloquium des Max-Planck-Instituts f{\"u}r Dynamik komplexer technischer Systeme, Magdeburg, Januar 1999}, volume = {1999}, booktitle = {Kolloquium des Max-Planck-Instituts f{\"u}r Dynamik komplexer technischer Systeme, Magdeburg, Januar 1999}, year = {1999}, subject = {Robuste Regelung}, language = {de} } @unpublished{Zindler1994, author = {Zindler, Klaus}, title = {Entwurf einer robusten Regelung nach der µ-Synthese am Beispiel eines prim{\"a}rseitig verstellbaren Hydrogetriebes}, year = {1994}, subject = {Robuste Regelung}, language = {de} } @unpublished{ZindlerKorn1996, author = {Zindler, Klaus and Korn, Ulrich}, title = {An H∞-based approach to complete modal synthesis ensuring robust steady state accuracy}, year = {1996}, subject = {Robuste Regelung}, language = {de} } @unpublished{KornZindler1995, author = {Korn, Ulrich and Zindler, Klaus}, title = {Vollst{\"a}ndige Modale Synthese robuster Regelungen mittels Optimierung eines H∞-Kostenfunktionals}, year = {1995}, subject = {Robuste Regelung}, language = {de} } @misc{ZindlerHahn2012, author = {Zindler, Klaus and Hahn, Stefan}, title = {Automatisierter Test von Fahrzeugsicherheitssystemen}, series = {Seminar "Fahrzeugregel- und Fahrzeugsicherheitssysteme"}, volume = {2012}, journal = {Seminar "Fahrzeugregel- und Fahrzeugsicherheitssysteme"}, number = {November}, address = {Obernburg}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {de} } @article{HahnHeinleinZindler2012, author = {Hahn, Stefan and Heinlein, Sven and Zindler, Klaus}, title = {Testing Methods for Forward-looking Safety Systems}, series = {ATZ worldwide}, volume = {114 (2012)}, journal = {ATZ worldwide}, number = {11}, publisher = {Springer Automotive Media}, pages = {4 -- 7}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {de} } @article{ZindlerBlank2014, author = {Zindler, Klaus and Blank, Andreas}, title = {Fußg{\"a}ngerattrappen mit Lineareinheiten realistisch bewegen}, series = {Konstruktion - Sonderausgabe "Special Antriebstechnik"}, volume = {2014}, journal = {Konstruktion - Sonderausgabe "Special Antriebstechnik"}, number = {S2}, publisher = {Springer-VDI-Verlag}, pages = {44 -- 45}, year = {2014}, subject = {Fahrerassistenzsystem}, language = {de} } @inproceedings{KranzHahnZindler2016, author = {Kranz, Tobias and Hahn, Stefan and Zindler, Klaus}, title = {Nonlinear Lateral Vehicle Control in Combined Emergency Steering and Braking Maneuvers}, series = {IEEE Intelligent Vehicles Symposium 2016, Gothenburg, Sweden, 19-22 June, 2016}, volume = {2016}, booktitle = {IEEE Intelligent Vehicles Symposium 2016, Gothenburg, Sweden, 19-22 June, 2016}, doi = {10.1109/IVS.2016.7535449}, pages = {603 -- 610}, year = {2016}, language = {en} } @inproceedings{HahnZindlerJumar2016, author = {Hahn, Stefan and Zindler, Klaus and Jumar, Ulrich}, title = {Two-Degrees-of-Freedom Lateral Vehicle Control using Nonlinear Model Based Disturbance Compensation}, series = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, volume = {49}, booktitle = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, number = {11}, doi = {10.1016/j.ifacol.2016.08.028}, pages = {182 -- 189}, year = {2016}, language = {en} } @inproceedings{ZindlerGeiss2016, author = {Zindler, Klaus and Geiß, Niklas}, title = {Vehicle Ego-Localization in Autonomous Lane-Keeping Evasive Maneuvers}, series = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, volume = {49}, booktitle = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, number = {11}, doi = {10.1016/j.ifacol.2016.08.025}, pages = {160 -- 167}, year = {2016}, language = {en} } @article{HahnZindlerJumar2016, author = {Hahn, Stefan and Zindler, Klaus and Jumar, Ulrich}, title = {Nichtlineare Spurf{\"u}hrung benutzerdefinierter Punkte an der Fahrzeugfront}, series = {at - Automatisierungstechnik}, volume = {64}, journal = {at - Automatisierungstechnik}, number = {4}, publisher = {de Gruyter Oldenbourg}, address = {M{\"u}nchen}, doi = {10.1515/auto-2015-0098}, pages = {282 -- 296}, year = {2016}, subject = {Nichtlineare Regelung}, language = {de} } @inproceedings{SauerZindlerGorksetal.2021, author = {Sauer, Timm and Zindler, Klaus and Gorks, Manuel and Spielmann, Luca and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using self-learning controllers considering a priori plant knowledge}, series = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, booktitle = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, year = {2021}, abstract = {This paper presents a new self-learning control scheme for lateral track guidance of industrial trucks using artificial intelligence. It is an universally applicable lateral dynamic control concept which is able to adapt itself to different truck variants. Moreover it shall consider vehicle parameter variations that occur during operation, such as the load dependent change of vehicle mass and moment of inertia. The proposed approach uses Reinforcement Learning. In order to reduce the training effort, a new concept is realized, taking into account a priori knowledge of vehicle behavior. Its fundamental idea consists of dividing the training process into two steps. In the first step the controller will be pre-trained on basis of a nominal model representing a priori knowledge of lateral dynamic vehicle behavior. Since this model is derived for an industrial truck with average vehicle parameter values, a fine tuning of the control parameters has to be performed in the second step. In this way the controller is adapted to the actual truck variant and the corresponding vehicle parameter values. In order to demonstrate the efficiency of the proposed control scheme, the simulation results given in this paper are compared to the closed loop behavior using standard LQR.}, subject = {Flurf{\"o}rderer}, language = {en} } @inproceedings{SauerGorksSpielmannetal.2022, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks with time-variant vehicle parameters using AI-based controllers}, series = {Advanes in Automotive Control - AAC}, volume = {2022}, booktitle = {Advanes in Automotive Control - AAC}, pages = {7}, year = {2022}, abstract = {This paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only finetuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a RL control concept, not considering time-variant parameters.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SauerSpielmannGorksetal.2022, author = {Sauer, Timm and Spielmann, Luca and Gorks, Manuel and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using AI-based controllers with disturbance compensation}, series = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, booktitle = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, year = {2022}, abstract = {This paper presents a new control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significanlty improves the control quality. In order to demonstrate this, the new approach is compared to a RL control concept, not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} }