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This dissertation presented a homogeneous higher order sliding mode control algorithm for a class underactuated system in presence of parameter variations and external disturbances. Based on the second order sliding mode control scheme, a new design methodology is proposed to tackle both the underactuated system's stabilization and tracking problems by Lyapunov direct method and hierarchal strategy. Theoretical analysis and simulation results demonstrates the advantages of the strategy and show that the method can achieve the tracking maneuver precision positioning and get global stability and robustness at the same time.

Fast catamaran holds excellent performance because of its high transverse stability, large deck, economy of fuel consumption and high speed. However, the high-speed craft can easily make "twist" oscillation in moderate to rough sea conditions due to its low length to beam ratio and relatively lightweight. T-foils or interceptors are effective stabilizers for alleviating the pitch and heave motions of fast craft. This work designs an autonomous catamaran motion control system utilizing 2 T-foils and 2 interceptors, which can not only control the longitudinal motion, but also alleviate the roll motion of fast catamaran. Numerical simulation results show that the motion control system can satisfactorily improve sea keeping performance of fast catamaran.

It is significant and valuable to improve the time length and accuracy of ships' motion prediction for the efficiency, comfort and security of marine operation. Autoregressive time series analysis method (AR) is the mainstream currently and the effectiveness for the prediction of ships' motion attitudes has been fully validated. However, the algorithm fixes the order only once but forecasts the future data for multi-step, result in degradation of the step length and accuracy, especially when the ship sails in the bad sea condition. In order to solve this issue, this paper proposes a new autoregressive-multiple method (Arm), which can determine the orders and parameters of model in a real-time. The method is applied to forecast a ship's motion attitudes in eight different situations. The simulative results of autoregressive-multiple method show the validity and veracity.

In this paper, we utilize both Fault Tree Analysis (FTA) and Bayesian networks (BN) to analyze the safety of the vessel's oil plants' static electricity systematically. We use FTA to show the failure model of the system, namely the causal and generalization relations between events and BN to model the polymorphism and uncertainty of these events showed in the Fault Tree. By utilizing the BN methodology, the limitations and deficiencies of FTA can be made up of — the BN method can eliminate the restrictive assumptions in FTA. In the electrostatic safety analysis of vessel's oil system, we combine FTA and BN in order to enhance the analytic accuracy and according to the analytic result, we propose some measures about Electrostatic Protection. The analytic technique we propose is certified that it can fit with the Actual Engineering better.