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On-line motion profile optimization for reciprocating mechanisms

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Abstract
Reciprocating mechanisms are widely used in industry because a complex movement is achieved by a simple rotation of the driven axis. Given the tendency to evolve towards more energy-efficient machines and flexible production, motion profile optimization offers a cost-effective solution as it results in large energy savings without any hardware adaptions. However, the existing optimizers are used off-line because the position-dependent parameters such as load torque and inertia of the system model must be known in advance. When the actual machine differs from the model, or when parameters change during operation due to process flexibility, the off-line determined motion profile is no longer optimal and results in unnecessary energy consumption. This paper therefore presents an on-line approach in which the varying inertia is estimated on the actual machine and used for updating the motion profile. The sliding discrete Fourier transform is proposed for real-time estimation and a gradient-based algorithm combined with Chebyshev polynomials is proposed for on-line optimization. Experimental validation on an industrial pick-and-place unit proves that the presented method leads to similar energy savings as off-line optimizers, but without prior knowledge of the parameters, and is moreover capable of handling mass changes during operation.
Keywords
Multi-body dynamics, Parameter estimation, Gradient-based optimization, Inertia variation, Motion control, Sliding discrete Fourier transform (SDFT)

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MLA
Vanbecelaere, Foeke, et al. “On-Line Motion Profile Optimization for Reciprocating Mechanisms.” MECHANISM AND MACHINE THEORY, vol. 173, 2022, doi:10.1016/j.mechmachtheory.2022.104833.
APA
Vanbecelaere, F., Van Oosterwyck, N., Derammelaere, S., Cuyt, A., Monte, M., & Stockman, K. (2022). On-line motion profile optimization for reciprocating mechanisms. MECHANISM AND MACHINE THEORY, 173. https://doi.org/10.1016/j.mechmachtheory.2022.104833
Chicago author-date
Vanbecelaere, Foeke, Nick Van Oosterwyck, Stijn Derammelaere, Annie Cuyt, Michael Monte, and Kurt Stockman. 2022. “On-Line Motion Profile Optimization for Reciprocating Mechanisms.” MECHANISM AND MACHINE THEORY 173. https://doi.org/10.1016/j.mechmachtheory.2022.104833.
Chicago author-date (all authors)
Vanbecelaere, Foeke, Nick Van Oosterwyck, Stijn Derammelaere, Annie Cuyt, Michael Monte, and Kurt Stockman. 2022. “On-Line Motion Profile Optimization for Reciprocating Mechanisms.” MECHANISM AND MACHINE THEORY 173. doi:10.1016/j.mechmachtheory.2022.104833.
Vancouver
1.
Vanbecelaere F, Van Oosterwyck N, Derammelaere S, Cuyt A, Monte M, Stockman K. On-line motion profile optimization for reciprocating mechanisms. MECHANISM AND MACHINE THEORY. 2022;173.
IEEE
[1]
F. Vanbecelaere, N. Van Oosterwyck, S. Derammelaere, A. Cuyt, M. Monte, and K. Stockman, “On-line motion profile optimization for reciprocating mechanisms,” MECHANISM AND MACHINE THEORY, vol. 173, 2022.
@article{8748964,
  abstract     = {{Reciprocating mechanisms are widely used in industry because a complex movement is achieved by a simple rotation of the driven axis. Given the tendency to evolve towards more energy-efficient machines and flexible production, motion profile optimization offers a cost-effective solution as it results in large energy savings without any hardware adaptions. However, the existing optimizers are used off-line because the position-dependent parameters such as load torque and inertia of the system model must be known in advance. When the actual machine differs from the model, or when parameters change during operation due to process flexibility, the off-line determined motion profile is no longer optimal and results in unnecessary energy consumption.

This paper therefore presents an on-line approach in which the varying inertia is estimated on the actual machine and used for updating the motion profile. The sliding discrete Fourier transform is proposed for real-time estimation and a gradient-based algorithm combined with Chebyshev polynomials is proposed for on-line optimization. Experimental validation on an industrial pick-and-place unit proves that the presented method leads to similar energy savings as off-line optimizers, but without prior knowledge of the parameters, and is moreover capable of handling mass changes during operation.}},
  articleno    = {{104833}},
  author       = {{Vanbecelaere, Foeke and Van Oosterwyck, Nick and Derammelaere, Stijn and Cuyt, Annie and Monte, Michael and Stockman, Kurt}},
  issn         = {{0094-114X}},
  journal      = {{MECHANISM AND MACHINE THEORY}},
  keywords     = {{Multi-body dynamics,Parameter estimation,Gradient-based optimization,Inertia variation,Motion control,Sliding discrete Fourier transform (SDFT)}},
  language     = {{eng}},
  pages        = {{19}},
  title        = {{On-line motion profile optimization for reciprocating mechanisms}},
  url          = {{http://dx.doi.org/10.1016/j.mechmachtheory.2022.104833}},
  volume       = {{173}},
  year         = {{2022}},
}

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