Estimation of Ship Properties for Energy Efficient Automation

Abstract: One method to increase efficiency, robustness and accuracy of automatic control, is to introduce mathematical models of the system in question to increase performance. With these models, it is possible to predict the behavior of the system, which enables control according to the predictions. The problem here is that if these models do not describe the dynamics of the system well enough, this method could fail to increase performance. To address this problem, one idea is to estimate the dynamics of the system during operation, using methods for system identification, signal processing and sensor fusion. In this thesis, the possibilities of estimating a ship's dynamics during operation have been investigated. The mathematical model describing the dynamics of the ship is a graybox model, which is based on the physical and mechanical relations. This model's properties are therefore described by physical quantities such as mass and moment of inertia, all of which are unknown. This means that, when estimating the model, these physical properties will be estimated. For a systematic approach, first a simulation environment with a 4-degrees-of-freedom ship model has been developed. This environment has been used for validation of system identification methods. A model of a podded propulsion system has also been derived and validated. The methods for estimating the properties of the ship have been analyzed using the data collected from the simulations. For system identification and estimation of ship properties, the influence of measurement noise and potential of detecting a change in dynamics has been analyzed. This has been done through Monte Carlo simulations of the estimation method with different noise realizations in the simulations, to analyze how the measurement noise affects the variance and bias for the estimates. The results show that variance and bias vary a lot between the parameters and that even a small change in dynamics is visible in some parameter estimates when only ten minutes of data have been used. A method based on cumulative summation (CUSUM) has been proposed and validated to analyze if such a method could yield fast and effective detection of system deviations. The results show that the method is rather effective a with robust detection of changes in the dynamics after about four minutes of data collection. Finally, the methods have been validated on data collected on a real ship to analyze the potential of the methods under actual circumstances. The results show that the particular data is not appropriate for this kind of application along with some additional problems that can yield impaired results.