Valentin Leipe

Herr M.Sc.

Wissenschaftlicher Mitarbeiter "Antriebssysteme und -regelung"
Institut für Steuerungstechnik der Werkzeugmaschinen und Fertigungseinrichtungen

Kontakt

+49 711 685 84527
E-Mail
Visitenkarte (VCF)

Seidenstr. 36
70174 Stuttgart
Deutschland
Raum: 5.093

Sprechstunde

nach Vereinbarung

Fachgebiet

Dynamik- und Genauigkeitssteigerung von Antriebssystemen
Neue Methoden der Antriebsregelung
Systemidentifikation
Modellbasierte Regelungstechnik

Publikationen

Meine Veröffentlichungen:

2024
1. Dietrich, David ; Jud, Oliver ; Kurth, Daniel ; Lechler, Armin ; Leipe, Valentin ; Nistler, Maximilian ; Reiff, Colin ; Schulte, Alexander ; u. a.: MoNA – Die modulare Werkzeugmaschine – Eine offene Forschungsplattform/MoNA – The modular machine tool – An open research platform. In: wt Werkstattstechnik online Bd. 114, VDI Fachmedien GmbH and Co. KG (2024), S. 170–177
  BibTeX
  @article{dietrich2024modulare, author = {Dietrich, David and Jud, Oliver and Kurth, Daniel and Lechler, Armin and Leipe, Valentin and Nistler, Maximilian and Reiff, Colin and Schulte, Alexander and Steinle, Lukas and Verl, Alexander}, doi = {10.37544/1436–4980–2024–05–6}, journal = {wt Werkstattstechnik online}, month = {05}, number = 05, pages = {170-177}, publisher = {VDI Fachmedien GmbH and Co. KG}, title = {MoNA – Die modulare Werkzeugmaschine – Eine offene Forschungsplattform/MoNA – The modular machine tool – An open research platform}, url = {/brokenurl#doi.org/10.37544/1436–4980–2024–05–6}, volume = 114, year = 2024 }
  Link
  /brokenurl#doi.org/10.37544/1436–4980–2024–05–6
  DOI
  10.37544/1436–4980–2024–05–6
2. Steinle, Lukas ; Leipe, Valentin ; Lechler, Armin ; Verl, Alexander: Automatic preload adaptation for rack-and-pinion drives to maximize performance and energy efficiency. In: Production Engineering (2024)
  BibTeX
  @article{Steinle2024, abstract = {Electrically preloaded rack-and-pinion drives are typically utilized in machine tools to precisely move heavy loads over long travel distances. In the state of the art, the preload torque is commonly set to a constant value during commissioning. This poses a conflict of objectives between maximizing performance and minimizing energy consumption. To resolve this, this publication presents a novel approach to automatically adapt the preload torque during operation. For this purpose, the established preload control is extended by a simple control law that adapts the preload torque to the current operating state within the permissible limits. This way, higher preload torques are only applied, if the resulting higher system stiffness is beneficial for the drive performance. Otherwise, the preload torque is reduced to save energy. The automatic preload adaptation is experimentally validated on a test system with industry standard components. This involves both system-theoretical analyses and practical test scenarios.}, author = {Steinle, Lukas and Leipe, Valentin and Lechler, Armin and Verl, Alexander}, day = 19, doi = {10.1007/s11740-024-01318-7}, issn = {1863-7353}, journal = {Production Engineering}, month = {11}, title = {Automatic preload adaptation for rack-and-pinion drives to maximize performance and energy efficiency}, url = {https://doi.org/10.1007/s11740-024-01318-7}, year = 2024 }
  Link
  https://doi.org/10.1007/s11740-024-01318-7
  DOI
  10.1007/s11740-024-01318-7
3. Steinle, Lukas ; Leipe, Valentin ; Lechler, Armin ; Veri, Alexander: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives. In: 2024 European Control Conference (ECC), 2024, S. 2441–2447
  - BibTeX
  - DOI
  BibTeX
  @inproceedings{10591213, abstract = {Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over long travel distances. A key concern in this context is the achievable path accuracy, which is limited by assembly and manufacturing tolerances of the gearing components in conjunction with load-dependent deformation and the inherent backlash of the system. To address this issue, this paper presents a method for robust modeling of the individual and state-dependent transmission errors of a drive utilizing a two-stage machine learning approach. Based on this, the position control is extended to include an error compensation, which suppresses the modeled deviations in the mechanical system including the position-dependent backlash. The achievable increase in path accuracy as well as the robustness of the approach are evaluated and quantified by an experimental validation on a system with industry standard components.}, author = {Steinle, Lukas and Leipe, Valentin and Lechler, Armin and Veri, Alexander}, booktitle = {2024 European Control Conference (ECC)}, doi = {10.23919/ECC64448.2024.10591213}, month = {06}, pages = {2441-2447}, title = {Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives}, year = 2024 }
  DOI
  10.23919/ECC64448.2024.10591213
4. Leipe, Valentin ; Steinle, Lukas ; Bikuña, Xabier Agirre ; Lechler, Armin ; Verl, Alexander: Modeling and Identification of Load-Dependent Properties for Electrically Preloaded Rack-and-Pinion Drives. In: 2024 European Control Conference (ECC), 2024, S. 2460–2466
  - BibTeX
  - DOI
  BibTeX
  @inproceedings{10590860, abstract = {Rack-and-pinion drives are preferred feed drives for long travel distances and heavy loads in large machine tools. One of the advantages compared to other feed drive systems is consistent stiffness regardless of travel length. A primary challenge with rack-and-pinion drives is the achievable accu-racy due to backlash. To compensate for backlash, electrically preloaded systems are commonly used in machine tools. In the case of electrical preload between two motors, the system is more complex to identify because the feed drive specific properties cannot be directly assigned to the respective drive train. To address this issue, this paper presents a novel method for modeling and identifying the load-dependent stiffness and damping of an electrically preloaded system. For this purpose, a mathematical modeling based on experimental data from a test bench with industrial components is presented to separate the drive train specific properties. This allows the system behavior to be modeled more accurately and used for control approaches.}, author = {Leipe, Valentin and Steinle, Lukas and Bikuña, Xabier Agirre and Lechler, Armin and Verl, Alexander}, booktitle = {2024 European Control Conference (ECC)}, doi = {10.23919/ECC64448.2024.10590860}, month = {06}, pages = {2460-2466}, title = {Modeling and Identification of Load-Dependent Properties for Electrically Preloaded Rack-and-Pinion Drives}, year = 2024 }
  DOI
  10.23919/ECC64448.2024.10590860
5. Verl, Alexander ; Leipe, Valentin: Dual motor position feedback control for electrically preloaded rack-and-pinion drive systems to increase accuracy. In: CIRP Annals Bd. 73, Elsevier BV (2024), S. 313–316
  BibTeX
  @article{Verl_2024, author = {Verl, Alexander and Leipe, Valentin}, doi = {10.1016/j.cirp.2024.04.087}, issn = {0007-8506}, journal = {CIRP Annals}, number = 1, pages = {313–316}, publisher = {Elsevier BV}, title = {Dual motor position feedback control for electrically preloaded rack-and-pinion drive systems to increase accuracy}, url = {http://dx.doi.org/10.1016/j.cirp.2024.04.087}, volume = 73, year = 2024 }
  Link
  http://dx.doi.org/10.1016/j.cirp.2024.04.087
  DOI
  10.1016/j.cirp.2024.04.087
2023
1. Leipe, Valentin ; Hinze, Christoph ; Lechler, Armin ; Verl, Alexander: Model predictive control for compliant feed drives with offset-free tracking behavior. In: Production Engineering (2023)
  BibTeX
  @article{Leipe2023, abstract = {Industrial machine tool feed drives are predominantly controlled by cascade control due to their low tuning complexity and inherent robustness. However, the cascaded structure requires the inner cascades to have higher dynamics than the outer cascades, which limits the achievable dynamic accuracy. Direct control approaches, which substitute the position and velocity cascade, offer the potential to utilize the unused potential. A promising approach is model predictive control (MPC), which optimizes the manipulated variable with a plant model along a prediction horizon. However, model uncertainties between the nominal model and the real plant lead to tracking errors. Therefore, this paper presents, a linear MPC (LMPC) and an adaptive MPC (AMPC) with an additional integral action to robustly compensate for model mismatches. Both controllers use a compliant model, are real-time capable with a sample rate of {\$}{\$}{\{}{\{}2{\}}{\backslash},{\backslash}textrm{\{}kHz{\}}{\}}{\$}{\$}and consider state and input space constraints. The AMPC accounts for position-varying stiffness and friction. The controllers are experimentally compared with classical P-PI cascaded control on a ball screw drive. They show a tracking error reduction of {\$}{\$}{\{}{\{}37{\}}{\{}{\backslash},{\backslash}{\%}{\}}{\}}{\$}{\$}(LMPC) and {\$}{\$}{\{}{\{}44{\}}{\{}{\backslash},{\backslash}{\%}{\}}{\}}{\$}{\$}(AMPC) during a high speed motion profile and an increase in bandwidth of {\$}{\$}{\{}{\{}180{\}}{\{}{\backslash},{\backslash}{\%}{\}}{\}}{\$}{\$}(LMPC) and {\$}{\$}{\{}{\{}184{\}}{\{}{\backslash},{\backslash}{\%}{\}}{\}}{\$}{\$}(AMPC), resulting in significantly improved dynamic accuracy.}, author = {Leipe, Valentin and Hinze, Christoph and Lechler, Armin and Verl, Alexander}, day = 22, doi = {10.1007/s11740-023-01206-6}, issn = {1863-7353}, journal = {Production Engineering}, month = {05}, title = {Model predictive control for compliant feed drives with offset-free tracking behavior}, url = {https://doi.org/10.1007/s11740-023-01206-6}, year = 2023 }
  Link
  https://doi.org/10.1007/s11740-023-01206-6
  DOI
  10.1007/s11740-023-01206-6
2. Steinle, Lukas ; Leipe, Valentin ; Lechler, Armin ; Verl, Alexander: Effizientere Antriebe durch smarte Steuerung. In: KG, V. C. G. G. &. Co. (Hrsg.) MM Maschinenmarkt (2023), S. 51–53
  - BibTeX
  BibTeX
  @article{maschinenmarkt2023effizientere, author = {Steinle, Lukas and Leipe, Valentin and Lechler, Armin and Verl, Alexander}, editor = {KG, Vogel Communications Group GmbH & Co.}, journal = {MM Maschinenmarkt}, number = 10, pages = {51-53}, title = {Effizientere Antriebe durch smarte Steuerung}, year = 2023 }

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