Digital Twin-Based Optimization for Ultraprecision Motion Systems With Backlash and Friction
Open accessAbstract
A digital twin-based optimization procedure is presented for an ultraprecision motion system with a flexible shaft connecting the motor to the (elastic) load, which is subject to both backlash and friction. The main contributions of the study are the design of the digital twin and its implementation, assuming a two-mass drive system. The procedure includes the virtual representation of mechanical and electrical components, non-linearities (backlash and friction), and the corresponding control system. A procedure for digital twin-based optimization is also presented, in which the maximum absolute position error is minimized while maintaining accuracy with no significant increase in the control effort. The optimal settings for the controller parameters and for the backlash peak amplitude, the backlash peak time, and the hysteresis amplitude are then determined, in order to guarantee an appropriate dynamic response in the presence of backlash and friction. The surface quality of certain manufactured components, such as hip and knee implants, depends on the smoothness and the accuracy of the real trajectory produced in the cutting process that is strongly influenced by the maximum position error. The simulations and experimental studies are presented using a real platform and two references for trajectory control, and a comparison of four digital twin-based optimization methods. The simulation study and the real-time experiments demonstrate the suitability of the digital twin-based optimization procedure and lay the foundations for the implementation of the proposed method at an industrial level.
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The digital twin concept is key for modeling, simulating and optimizing cyber-physical systems.
“The DT concept is key for modeling, simulating and optimizing cyber-physical systems [58].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [58]
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- Authors
- Guerra, R. H., Quiza, R., Villalonga, A., Arenas, J., & Castano, F.
- Affiliations
- Centre for Automation and Robotics (ES)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 93462-93472
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2961073176
- Cited by
- 89
- References
- 43
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 6 · 2023: 16 · 2022: 17 · 2021: 14 · 2020: 23 · 2019: 2
- FWCI
- 7.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Hispana (acceptedVersion, cc-by-nc-nd)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UPM Digital Archive (Technical University of Madrid) (submittedVersion, cc-by-nc-nd)
- Greater South Information System
- Funders
- H2020 ECSEL (826417)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Control Systems in Engineering
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Control and Systems Engineering › Dynamics and Control of Mechanical Systems
- Keywords
- digital twin, backlash, friction, flexible shaft, controller tuning, hysteresis, trajectory control, hip and knee implants, cutting process, surface quality
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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