Irvine cable equations and neural networks - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

Irvine cable equations and neural networks

Abstract

A classical planar cable model has been presented in the Irvine textbook and is being used, for example, for the modeling of cable-driven parallel robot. It provides 2 equations involving parameters of the cable material, namely its Young modulus and its linear density, assumed here to be known, and 5 physical parameters namely the coordinates of one cable end-point B, the horizontal and vertical components of the force exerted at B and the length at rest of the cable. This model is extensively used in the modeling of devices where cables are involved (e.g the kinematics of cable-driven parallel robots) and the model analysis requires to be able to solve very efficiently this 2-equations system when it has 1 or 2 unknowns. We consider various cases where n physical parameters are known and we investigate how the Irvine equations may be exploited to compute the 5 − n remaining parameters. In some cases where n < 3it is possible to determine a closed-form solution for this 5 − n parameters. But if n = 3 it appears that only a numerical approach may allow to get the 2 remaining parameters. We present here a generic algorithm based on a mix of neural networks and deterministic algorithms allows one to get exact solution in that case.
Fichier principal
Vignette du fichier
iftomm_wc_2023_v2.pdf (153.38 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04170366 , version 1 (25-07-2023)

Licence

Attribution

Identifiers

  • HAL Id : hal-04170366 , version 1

Cite

Jean-Pierre Merlet. Irvine cable equations and neural networks. IFToMM WC 2023 - 16th World Congress of the International Federation for the Promotion of Mechanism and Machine Science, Tokyo Institute of Technology, Keio University, Nov 2023, Tokyo, Japan. ⟨hal-04170366⟩
116 View
43 Download

Share

Gmail Facebook X LinkedIn More