Algorithm Simulation of Ship Dynamic Positioning Using Adaptive Fading Memory Filter

作者：Zhang, Shan; Zou, Zao Jian

来源：Journal of Ship Mechanics, 2017, 21(12): 1497-1506.

DOI：10.3969/j.issn.1007-7294.2017.12.006

• Algorithm simulation
• Controller outputs
• Efficient control
• Fading memory filter
• Low-frequency motion
• Positioning precision
• Ship dynamic positioning
• State estimation algorithms

Due to the complexity and nonlinearity of ship motion at seas, an accurate mathematical model for ship dynamic positioning system is difficult to establish. In order to achieve efficient control, it is necessary to obtain the required signals of low frequency motion by means of a filter algorithm for state estimation. Using the conventional Kalman filter, the correction effect of new measurement data of state variables on the prediction decreases, while the influence of the old measurement data increases with the time step, which is the main reason of filter divergence. To solve the problem of inaccurate model, inaccurate expression of system noises and measurement noises when applying Kalman filter in a ship dynamic positioning system, an adaptive fading memory filter is employed to estimate the low frequency motion. By introducing the fading memory factor in the state estimation algorithm, the effect weight of the old measurement data on the state estimation is decreased, and the impact of the new measurement data is increased. Besides, according to the criterion for filter divergence, a proper fading memory factor is chosen to restrain the filter divergence and to make the controller output relatively smooth, so that the unnecessary energy consumption of the thruster system is reduced. The simulation results show that the designed adaptive filter is superior to Kalman filter in convergence and traceability, and the positioning precision and stability of the system are effectively improved.

• 出版日期2017
• 单位上海交通大学