V.J. Kurian

Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter

This paper proposes three steps of improvements for identification of the nonlinear dynamic system, which exploits the concept of a state-space based time domain Volterra model. The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel 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). The applicability of the proposed methods is tested in three simulated data and one experimental data. The results show that Volterra model with PSO–KS is preferable for fast identification process, while ABC–KS method is preferable for accurate identification process. However, in some cases, as the iteration number increases the result of PSO–KS method is comparable with ABC–KS method.

Prediction of Surge Motion Response of Floating Structure Using Kalman Smoother Adaptive Filters Based Time-Domain Volterra Model

This work presents an approachto predict the low frequency and wave frequency responses (LFR and WFR) of afloating structure using Kalman smoother adaptive filters based time domain Volterramodel. This method utilized time series of a measured wave height as systeminput and surge motion as system output and used to generate the linear andnonlinear transfer function (TFs). Based on those TFs, predictions of surgemotion in terms of LFR and WFR were carried out in certain frequency ranges ofwave heights. The applicability of the proposed method is then applied in ascaled 1:100 model of a semisubmersible prototype.

Nonlinear System Identification of Floating Structures Using Time-Varying ARX-Based Volterra Model

This work presents a newapproach for nonlinear system identification of floating structures using time-varyingautoregressive with exogenous input (TVARX)–based Volterra model. This methodutilizes time series of measured wave heights as system input and surge motionas system output. Solution to the proposed approach is also proposed via Kalmansmoother algorithm. The efficacy of the proposed method is then applied in ascaled 1:100 model of a prototype truss spar. It is shown that surge motionobtained from the identification results has good agreement with theexperimental results either in time or frequency domain