Identification of a scaled-model riser dynamics through a combined computer vision and adaptive Kalman filter approach

•A projective geometry computer vision method maps object space into image space.•Image coordinates were obtained through a mathematical morphology base algorithm.•A compliant mechanism and a Lagrangian formulation was employed to model the riser.•A Kalman filter used image coordinates to identify riser dynamics and a forcing term.

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.