Identification of a scaled-model riser dynamics through a combined computer vision and adaptive Kalman filter approach
Nenhuma Miniatura disponível
Citações na Scopus
15
Tipo de produção
Artigo
Data
2014-02-03
Autores
TRIGO, F. C.
MARTINS, F. P. R.
FLEURY, A. T.
SILVA JUNIOR, H. C.
MARTINS, F. P. R.
FLEURY, A. T.
SILVA JUNIOR, H. C.
Orientador
Periódico
Mechanical Systems and Signal Processing
Título da Revista
ISSN da Revista
Título de Volume
Citação
TRIGO, F. C.; MARTINS, F. P. R.; FLEURY, A. T.; SILVA JUNIOR, H. C. Identification of a scaled-model riser dynamics through a combined computer vision and adaptive Kalman filter approach. Mechanical Systems and Signal Processing, v. 43, n. 1-2, p. 124-140, feb. 2014.
Texto completo (DOI)
Palavras-chave
Resumo
Abstract Aiming at overcoming the difficulties derived from the traditional camera calibration methods to record the underwater environment of a towing tank where experiments of scaled-model risers are carried on, a computer vision method, combining traditional image processing algorithms and a self-calibration technique was implemented. This method was used to identify the coordinates of control-points viewed on a scaled-model riser submitted to a periodic force applied to its fairlead attachment point. To study the observed motion, the riser was represented as a pseudo-rigid body model (PRBM) and the hypotheses of compliant mechanisms theory were assumed in order to cope with its elastic behavior. The derived Lagrangian equations of motion were linearized and expressed as a state-space model in which the state variables include the generalized coordinates and the unknown generalized forces. The state-vector thus assembled is estimated through a Kalman Filter. The estimation procedure allows the determination of both the generalized forces and the tension along the cable, with statistically proven convergence. © 2013 Elsevier Ltd. All rights reserved.