State Estimation and Control for a Model Scale Passenger Ship using an LQG Approach

Abstract

Reducing the roll response of ships between irregular waves is an important issue for the operational requirement. This study presents a roll dynamics model for a passenger ship equipped with active fins. In this study, a Kalman Filter was applied to accurately estimate all states from the measurement of total roll motion and roll velocity (based on fins and waves), even in the presence of measurement noise. Synchronously, a linear quadratic gaussian (LQG) controller actively drives the fins to minimize roll motion and velocity by taking the fin amplitude and rate saturations together. Two different sea states were modeled for the simulation purpose. Results demonstrate the success of the state estimation approach and the remarkable potential of the LQG strategy in roll reduction.