云模型自适应算法在船舶航向控制系统中的应用

The application of cloud model adaptive algorithm in ship course control system

我国航运发达,船舶在航行的过程中会受到风浪流等不确定因素的影响,人们在提高航行安全性和可靠性方面进行了大量的研究。本文利用云模型对传统的粒子群算法进行优化,在优化过程中主要采用的是正态云粒子发生器,对种群中不同区间中的粒子,通过自适应的方式获取惯性权重值,并详细地阐述了算法的实现过程。最后利用Matlab进行系统仿真,在仿真的过程中增加了白噪声干扰,实验结果表明本文算法较PID控制鲁棒性强、调节速度快、能耗少。

With the development of shipping in our country,ships are affected by uncertain factors such as wind and wave during the course of navigation.A great deal of research has been done on reducing the safety of navigation and improving the safety of seafarers.This paper used cloud model to optimize the traditional particle swarm optimization algorithm.In the process of optimization,the normal cloud particle generator was adopted.The inertia weights were obtained adaptively for particles in different intervals in a population.Algorithm implementation process was described in detail.Finally,the system simulation was carried out by using matlab.In the process of simulation,white noise was increased.Experimental results showed that the proposed algorithm was more robust,faster and less energy consuming than PID control.