SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS

This study presents the identification of slow drift motions of floating structures from
model test data. To compute the slow drift motions, nonlinear and nonstationary system
identification which exploits the concept of a state-space based time domain input-ouput
models is proposed, comprising the time-varying nonlinear autoregressive with
exogenous input (TVNARX) and Volterra models. Three steps of improvements had
been made to increase the modeling capacity of input-output models. The first step is
presenting the backward estimator and combined forward-backward estimator instead of
the only forward estimator in the original input-output models; the second step is
reformulating the input-output models into a state-space model so that the Kalman
Smoother (KS) adaptive filter can be used to estimate the model coefficients; the third
step is optimization of KS parameters using evolutionary computing algorithms such as
Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony
(ABC) to form the PSO-KS, GA-KS and ABC-KS as estimation methods.