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Artykuły w czasopismach na temat „Contrôle feed-forward”

Autor: Grafiati
Data publikacji: 24 maja 2025
Data aktualizacji: 29 maja 2025

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1

Seifried, Robert, Held Alexander, and Dietmann Fabian. "58301 ANALYSIS OF FEED-FORWARD CONTROL DESIGNS FOR FLEXIBLE MULTIBODY SYSTEMS(Flexible Multibody Dynamics)." Proceedings of the Asian Conference on Multibody Dynamics 2010.5 (2010): _58301–1_—_58301–9_. http://dx.doi.org/10.1299/jsmeacmd.2010.5._58301-1_.

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Tutsch, R., and C. Hernández. "Feed-Forward-Regelung von Prozessketten*." wt Werkstattstechnik online 103, no. 11-12 (2013): 911–14. http://dx.doi.org/10.37544/1436-4980-2013-11-12-911.

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Sengupta, Poulami, Kalpana P, and Prakash P. "Fly Back Converter with Feed - Forward Control and TPS3700 based Protection Scheme for Fast Transient Space Applications." International Journal of Science and Research (IJSR) 10, no. 8 (2021): 342–45. https://doi.org/10.21275/sr21807161535.

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Yabui, Shota, and Takenori Atsumi. "MoD-1-3 IMPROVEMENT OF CONVERGENCE FOR ADAPTIVE FEED-FORWARD CONTROL BY INITIAL VALUE SETTING IN HARD DISK DRIVES." Proceedings of JSME-IIP/ASME-ISPS Joint Conference on Micromechatronics for Information and Precision Equipment : IIP/ISPS joint MIPE 2015 (2015): _MoD—1–3–1—_MoD—1–3–3. http://dx.doi.org/10.1299/jsmemipe.2015._mod-1-3-1.

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5

Char, M. B. S. "Questions concerned with feed‐back and feed‐forward cycles." Kybernetes 27, no. 1 (1998): 87–89. http://dx.doi.org/10.1108/03684929810795199.

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Norrlöf, Mikael, and Svante Gunnarsson. "An ILC approach to feed-forward friction compensation." IFAC-PapersOnLine 53, no. 2 (2020): 1409–14. http://dx.doi.org/10.1016/j.ifacol.2020.12.1902.

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Johansson, Viktor, Stig Moberg, Erik Hedberg, Mikael Norrlöf, and Svante Gunnarsson. "A Learning Approach for Feed-Forward Friction Compensation." IFAC-PapersOnLine 51, no. 22 (2018): 412–17. http://dx.doi.org/10.1016/j.ifacol.2018.11.578.

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8

Heyden, Martin, Richard Pates, and Anders Rantzer. "A Structured Optimal Controller With Feed-Forward for Transportation." IEEE Control Systems Letters 6 (2022): 1130–35. http://dx.doi.org/10.1109/lcsys.2021.3088666.

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9

Silva, Sergio, Leonardo Sampaio, Fernando Oliveira, and Fábio Durand. "Pso-based Mppt Technique Applied To A Grid-tied Pv System With Active Power Line Conditioning Using A Feed-forward Dc-bus Control Loop." Eletrônica de Potência 21, no. 2 (2016): 105–16. http://dx.doi.org/10.18618/rep.2016.2.2615.

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10

Rowold, Matthias, Alexander Wischnewski, and Boris Lohmann. "Constrained Bayesian Optimization of a Linear Feed-Forward Controller." IFAC-PapersOnLine 52, no. 29 (2019): 1–6. http://dx.doi.org/10.1016/j.ifacol.2019.12.612.

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11

Haas, Rainer, and Evgeny Lukachev. "Optimal feed-forward control of a digital hydraulic drive." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 231, no. 2 (2016): 94–106. http://dx.doi.org/10.1177/0959651816640622.

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12

Just, Fabian, Özhan Özen, Philipp Bösch, et al. "Exoskeleton transparency: feed-forward compensation vs. disturbance observer." at - Automatisierungstechnik 66, no. 12 (2018): 1014–26. http://dx.doi.org/10.1515/auto-2018-0069.

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Abstract Undesired forces during human-robot interaction limit training effectiveness with rehabilitation robots. Thus, avoiding such undesired forces by improved mechanics, sensorics, kinematics, and controllers are the way to increase exoskeleton transparency. In this paper, the arm therapy exoskeleton ARMin IV+ was used to compare the differences in transparency offered by using the previous feed-forward model-based controller, with a disturbance observer in a study. Systematic analysis of velocity-dependent effects of controller transparency in single- and multi-joint scenarios performed i
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13

Egami, Tadashi, and Takeshi Tsuchiya. "Efficiency-Optimized Speed Control System with Feed-Forward Compensation." IEEE Transactions on Industrial Electronics IE-34, no. 2 (1987): 216–26. http://dx.doi.org/10.1109/tie.1987.350957.

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14

Schmuck, Christian, Frank Woittennek, Albrecht Gensior, and Joachim Rudolph. "Feed-Forward Control of an HVDC Power Transmission Network." IEEE Transactions on Control Systems Technology 22, no. 2 (2014): 597–606. http://dx.doi.org/10.1109/tcst.2013.2253322.

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15

Erwin-Wright, Stephen, David Sanders, and Sheng Chen. "Predicting terrain contours using a feed-forward neural network." Engineering Applications of Artificial Intelligence 16, no. 5-6 (2003): 465–72. http://dx.doi.org/10.1016/j.engappai.2003.08.002.

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16

Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action r
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17

Marcello, Fabio Bonassi, and Farina Riccardo Scattolini. "Stability of discrete-time feed-forward neural networks in NARX configuration." IFAC-PapersOnLine 54, no. 7 (2021): 547–52. http://dx.doi.org/10.1016/j.ifacol.2021.08.417.

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18

Li, Yibo, Zhaohui Liu, and Yanmei Liu. "Hysteresis Modeling of Piezoelectric Actuators and Feed-Forward Compensation Algorithm Research." International Journal of Control and Automation 8, no. 12 (2015): 331–40. http://dx.doi.org/10.14257/ijca.2015.8.12.30.

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19

Fan, Bo, Zhumu Fu, Kai Michels, and Jiangtao Fu. "Coordinate strategy of dual-pulse-width-modulation converter based on direct power control with load power feed-forward." Measurement and Control 53, no. 5-6 (2020): 859–69. http://dx.doi.org/10.1177/0020294020912774.

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The common dual-pulse-width-modulation converter adopts the independent method to control the rectifier and inverter, which has the defects including direct current voltage fluctuation, slow system response and so on. A coordinate strategy of dual-pulse-width modulation based on direct power control with load power feed-forward is proposed in this paper. The fixed switching frequency method with space vector pulse width modulation is provided to overcome the instability of power devices, caused by the hysteresis comparators in the conventional direct power control. With the analysis on the dyn
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20

Rabbani, Tarek S., Florent Di Meglio, Xavier Litrico, and Alexandre M. Bayen. "Feed-Forward Control of Open Channel Flow Using Differential Flatness." IEEE Transactions on Control Systems Technology 18, no. 1 (2010): 213–21. http://dx.doi.org/10.1109/tcst.2009.2014640.

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21

Braun, Tristan, Johannes Reuter, and Joachim Rudolph. "Flatness-Based Feed-Forward Control Design for Solenoid Actuators Considering Eddy Currents." IFAC-PapersOnLine 52, no. 15 (2019): 567–72. http://dx.doi.org/10.1016/j.ifacol.2019.11.736.

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22

Machado, Eduardo, Tiago Pinto, Vanessa Guedes, and Hugo Morais. "Electrical Load Demand Forecasting Using Feed-Forward Neural Networks." Energies 14, no. 22 (2021): 7644. http://dx.doi.org/10.3390/en14227644.

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The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increas
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23

Vazquez, Nimrod, and Marco Liserre. "Peak Current Control and Feed-Forward Compensation of a DAB Converter." IEEE Transactions on Industrial Electronics 67, no. 10 (2020): 8381–91. http://dx.doi.org/10.1109/tie.2019.2949523.

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24

Chang, R. G., C. Y. Chen, J. H. Hong, and S. Y. Lee. "Wide dynamic-range sigma–delta modulator with adaptive feed-forward coefficients." IET Circuits, Devices & Systems 4, no. 2 (2010): 99. http://dx.doi.org/10.1049/iet-cds.2009.0079.

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25

Chinnam, Ratna Babu, and Pundarikaksha Baruah. "Empirical prediction limit estimation methods for feed-forward neural networks." International Journal of General Systems 36, no. 2 (2007): 221–36. http://dx.doi.org/10.1080/03081070600782170.

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26

Anas, Muhammad. "Probiotics Role in Control of Coccidiosis in Poultry Industry." Open Access Journal of Veterinary Science & Research 3, no. 1 (2018): 1–3. http://dx.doi.org/10.23880/oajvsr-16000152.

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Cereal products fermented by lactic acid bacteria are documented first in Egypt and Iraq during 2000 - 3000 B.C. These are one of the oldest fermented foods. In 1907, Elie Metcnikoff was the first scientist who not only observes but also put forward the scie ntific basics of fermentation. Then to explore gut bacteria intensive researches were made in late 1940s. In 2006 FAO and WHO give the complete definition of probiotics, living microbes beneficial for health provided in feed. For treatment of Coccidiosis p robiotic combinations of different microbes such as Lactobacillus, Bifidibacterium a
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27

Saravanan, N., A. Duyar, T. H. Guo, and W. C. Merrill. "Modeling space shuttle main engine using feed-forward neural networks." Journal of Guidance, Control, and Dynamics 17, no. 4 (1994): 641–48. http://dx.doi.org/10.2514/3.21250.

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28

Erikssont, Jerry, Mårten Gulliksson, Per Lindström, and Per-åke Wedin. "Regularization tools for training large feed-forward neural networks using automatic differentiation∗." Optimization Methods and Software 10, no. 1 (1998): 49–69. http://dx.doi.org/10.1080/10556789808805701.

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29

Bartl, Andreas, Morteza Karamooz Mahdiabadi, Christina Insam, Johannes Mayet, and Daniel J. Rixen. "A hybrid testing method based on adaptive feed-forward filters." Mechanical Systems and Signal Processing 139 (May 2020): 106586. http://dx.doi.org/10.1016/j.ymssp.2019.106586.

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30

Yuen, Shelten G., Douglas P. Perrin, Nikolay V. Vasilyev, Pedro J. del Nido, and Robert D. Howe. "Force tracking with feed-forward motion estimation for beating heart surgery." IEEE Transactions on Robotics 26, no. 5 (2010): 888–96. http://dx.doi.org/10.1109/tro.2010.2053734.

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31

Csikasz-Nagy, Attila. "1SA-06 Feedback and feed-forward controls of cell cycle transitions(1SA Dynamics and Robustness in Biological networks,The 49th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 51, supplement (2011): S1—S2. http://dx.doi.org/10.2142/biophys.51.s1_6.

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32

Bodrov, Alexey, Roger Shuttleworth, and Matteo Iacchetti. "Modulation and Feed-Forward Based Damping Matching Strategy for Linear Generators." IEEE Transactions on Industry Applications 57, no. 5 (2021): 5296–305. http://dx.doi.org/10.1109/tia.2021.3088087.

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33

Aalbers, A. B., R. F. Tap, and J. A. Pinkster. "An application of dynamic positioning control using wave feed forward." International Journal of Robust and Nonlinear Control 11, no. 13 (2001): 1207–37. http://dx.doi.org/10.1002/rnc.603.

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34

Correia, Sérgio D., Slavisa Tomic, and Marko Beko. "A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization." Journal of Sensor and Actuator Networks 10, no. 2 (2021): 29. http://dx.doi.org/10.3390/jsan10020029.

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The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work aims to show their potential use for this field of application. As such, the present work proposes a deep feed-forward neural network for solving the acoustic source localization problem based on energy measurements. Several network typologies are trained with ideal noise-free conditions, which simplifies the usual
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35

Kelly, Rafael. "A Linear-State Feedback Plus Adaptive Feed-Forward Control for DC Servomotors." IEEE Transactions on Industrial Electronics IE-34, no. 2 (1987): 153–57. http://dx.doi.org/10.1109/tie.1987.350947.

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36

Garone, Emanuele, Francesco Tedesco, and Alessandro Casavola. "Sensorless supervision of linear dynamical systems: The Feed-Forward Command Governor approach." Automatica 47, no. 7 (2011): 1294–303. http://dx.doi.org/10.1016/j.automatica.2011.01.034.

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37

Cuyper, Joris De, Michel Verhaegen, and Jan Swevers. "Off-line feed-forward and feedback control on a vibration rig." Control Engineering Practice 11, no. 2 (2003): 129–40. http://dx.doi.org/10.1016/s0967-0661(02)00103-x.

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38

Wang, Guo-Wen, Naofumi Fujiwara, and YUE BAO. "Feed-forward multilayer neural network model for vehicle lateral guidance control." Advanced Robotics 12, no. 7-8 (1997): 735–53. http://dx.doi.org/10.1163/156855399x00117.

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39

Wang, Zhihui, and Lixia Duan. "The combined effects of the thalamic feed-forward inhibition and feed-back inhibition in controlling absence seizures." Nonlinear Dynamics 108, no. 1 (2022): 191–205. http://dx.doi.org/10.1007/s11071-021-07178-5.

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40

Pena Ramirez, J., and H. Nijmeijer. "Enforcing synchronization in oscillators with Huygens’ coupling via feed-forward control." Nonlinear Dynamics 98, no. 4 (2019): 3009–23. http://dx.doi.org/10.1007/s11071-019-05062-x.

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41

Bharath, Y. K. "Griffiths’ Variable Learning Rate Online Sequential Learning Algorithm for Feed-Forward Neural Networks." Automatic Control and Computer Sciences 56, no. 2 (2022): 160–65. http://dx.doi.org/10.3103/s0146411622020031.

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42

Casavola, Alessandro, Emanuele Garone, and Francesco Tedesco. "Improved Feed-Forward Command Governor Strategies for Constrained Discrete-Time Linear Systems." IEEE Transactions on Automatic Control 59, no. 1 (2014): 216–23. http://dx.doi.org/10.1109/tac.2013.2270037.

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43

Chen, Y.-M., and K. F. Gill. "A Fuzzy Neural Controller Design for Use with a Non-Linear System." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 210, no. 2 (1996): 141–50. http://dx.doi.org/10.1243/pime_proc_1996_210_446_02.

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The control scheme presented utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both an identification and control role, and the latter is a fuzzy neural algorithm which is introduced to provide additional control enhancement. The feed-forward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rules automatically to improve the overall control action. To evaluate the performance of the controller, a simulated robot manipulator study was undertaken and the
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44

Kim, Yonghun, Hyunho Ye, Sun Lim, and Seok-Kyoon Kim. "Observer-Based Nonlinear Proportional–Integral–Integral Speed Control for Servo Drive Applications via Order Reduction Technique." Actuators 13, no. 1 (2023): 2. http://dx.doi.org/10.3390/act13010002.

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This study designs an advanced single-loop output feedback system for speed servo drive applications, in which a simple proportional–integral–integral (PII) controller equipped with nonlinear feedback and feed-forward gains is formed. The resultant feedback system shows the desired critically damped performance for wide-operating regions by actively handling the system parameter and load uncertainties. There are three contributions: first, the third-order observer estimates, independent from the system model, where the speed and acceleration are obtained using the position measurement with the
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45

Qin, Wei, Xinke Wu, and Junming Zhang. "Current-Feed Single-Switch Forward Resonant DC Transformer (DCX) With Secondary Diode-Clamping." IEEE Transactions on Industrial Electronics 64, no. 10 (2017): 7790–99. http://dx.doi.org/10.1109/tie.2017.2696462.

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46

Kamat, D. V., P. V. Ananda Mohan, and K. Gopalakrishna Prabhu. "Active-RC filters using two-stage OTAs with and without feed-forward compensation." IET Circuits, Devices & Systems 5, no. 6 (2011): 527. http://dx.doi.org/10.1049/iet-cds.2011.0092.

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47

Burkan, Recep. "Design of an adaptive control law using trigonometric functions for robot manipulators." Robotica 23, no. 1 (2005): 93–99. http://dx.doi.org/10.1017/s0263574704000657.

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In this study, a new approach of adaptive control law for controlling robot manipulators using the Lyapunov based theory is derived, thus the stability of an uncertain system is guaranteed. The control law includes a PD feed forward part and a full dynamics feed forward compensation part with the unknown manipulator and payload parameters. The novelty of the obtained result is that an adaptive control algorithm is developed using trigonometric functions depending on manipulator kinematics, inertia parameters and tracking error, and both system parameters and adaptation gain matrix are updated
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48

Wu, Jianhua, Chao Liu, YongJiang Liu, Zhenhua Xiong, and Han Ding. "Force ripple compensation of the directly-driven linear motors via iterative tuning feed-forward controller." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 9 (2019): 1239–47. http://dx.doi.org/10.1177/0959651819827707.

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Linear motors are promising in improving the manufacturing equipments’ performance because of eliminating the flexible coupling component. However, the force ripple produced by the linear motors directly causes the feed fluctuation and thus degrades the motion precision. This article utilizes a feed-forward controller added to the feedback one to compensate its effect for the sake of simplicity and robustness. Considering that the force ripple is periodic to the position, a position-dependent multi-order harmonic model is built and used as the feed-forward controller. In order to obtain the co
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49

Pfeiffer, Sven, Annika Eichler, and Holger Schlarb. "Model-based feed-forward control for time-varying systems with an example for SRF cavities." IFAC-PapersOnLine 53, no. 2 (2020): 1331–36. http://dx.doi.org/10.1016/j.ifacol.2020.12.1868.

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50

Bauer, Michel, and Oliver Sawodny. "Near time-optimal two-staged flatness based feed-forward control of a clutch filling process." IFAC-PapersOnLine 52, no. 15 (2019): 205–10. http://dx.doi.org/10.1016/j.ifacol.2019.11.675.

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