Zi-Hao Wang

Dan Zhang, Xi Zhou, Zi-Hao Wang et al.

Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamic characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long-crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method.

Hong-Yan Zhang, Lu-Sha Zhou, Zi-Hao Wang et al.

Solving quaternion kinematical differential equations is one of the most significant problems in the automation, navigation, aerospace and aeronautics literatures. Most existing approaches for this problem neither preserve the norm of quaternions nor avoid errors accumulated in the sense of long term time. We present symplectic geometric algorithms to deal with the quaternion kinematical differential equation by modeling its time-invariant and time-varying versions with Hamiltonian systems by adopting a three-step strategy. Firstly, a generalized Euler's formula for the autonomous quaternion kinematical differential equation are proved and used to construct symplectic single-step transition operators via the centered implicit Euler scheme for autonomous Hamiltonian system. Secondly, the symplecitiy, orthogonality and invertibility of the symplectic transition operators are proved rigorously. Finally, the main results obtained are generalized to design symplectic geometric algorithm for the time-varying quaternion kinematical differential equation which is a non-autonomous and nonlinear Hamiltonian system essentially. Our novel algorithms have simple algorithmic structures and low time complexity of computation, which are easy to be implemented with real-time techniques. The correctness and efficiencies of the proposed algorithms are verified and validated via numerical simulations.