基于渐消记忆自适应滤波的船舶动力定位算法仿真

由于船舶在海上运动的复杂性和非线性,精确的船舶动力定位系统数学模型难以建立.为了实现有效的动力定位控制,需要应用一定的状态估计滤波算法得到所需的船舶运动低频信号.采用常规的Kalman滤波,状态变量的新测量值对预测值的修正作用下降,旧测量值的影响随着计算步数的累积而相对提高,这是引起滤波发散的主要原因之一.文章针对船舶动力定位系统中使用常规的Kalman滤波而存在的模型不精确、 不能准确表达系统噪声和测量噪声等问题,采用渐消记忆自适应滤波估算低频运动信息,在状态估计算法中引入渐消记忆因子,减小旧测量值对状态估计值的影响权重,从而增大新测量值的作用;并根据滤波发散判断准则,选择适当的渐消记忆因子值来抑制滤波器的发散,使控制器输出较为平稳,从而降低推力系统不必要的能耗.仿真实验表明,所设计的自适应滤波器的收敛性、跟踪性优于常规的Kalman滤波,有效地提高了系统的定位精度和稳定性.

Algorithm Simulation of Ship Dynamic Positioning Using Adaptive Fading Memory Filter

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 con-trol, it is necessary to obtain the required signals of low frequency motion by means of a filter algo-rithm for state estimation. Using the conventional Kalman filter, the correction effect of new measure-ment 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 prob-lem of inaccurate model, inaccurate expression of system noises and measurement noises when ap-plying Kalman filter in a ship dynamic positioning system, an adaptive fading memory filter is em-ployed to estimate the low frequency motion. By introducing the fading memory factor in the state es-timation 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 fil-ter 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.