SLOW DRIFT MOTIONS IDENTIFICATION OF FLOATING STRUCTURES USING TIME-VARYING INPUT -OUTPUT MODELS - YAZID, EDWAR (2015)
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.