遗传粒子群优化算法在船舶动力定位控制中的应用  

Application of the genetic particle swarm optimization algorithm in dynamic positioning ship control

作　　者：薛彩霞[1] 袁伟[1] 俞孟蕻[1] 樊冀生 XUE Caixia;YUAN Wei;YU Menghong;FAN Jisheng(School of Electronic and Information,Jiangsu University of Science and Technology,Zhenjiang 212003,China)

机构地区：[1]江苏科技大学电子信息学院,江苏镇江212003

出　　处：《中国舰船研究》2016年第4期111-115,共5页Chinese Journal of Ship Research

基　　金：江苏省产学研联合创新资金资助项目(BY2013066-08);江苏高校高技术船舶协同创新中心/江苏科技大学海洋装备研究院资助项目(HZ2015006);江苏省科技支撑计划(工业)(BE2011149)

摘　　要：针对船舶动力定位系统精确定点控制的问题,结合遗传算法(GA)独特的选择交叉变异功能和粒子群优化算法(PSO)较好的记忆功能等优点,提出了遗传粒子群(GAPSO)算法,并应用到最优控制性能指标加权矩阵的权重系数选择中。通过1艘海工多用途动力定位船舶定点控制仿真实验,使船舶纵荡和横荡的位置及艏摇角都逐渐保持在期望值,且所有输出值都收敛有界,结果与传统最优控制相比,遗传粒子群算法在最优控制中更具有效性及较好的寻优性能,有益于船舶工程的应用。Aiming at the issue of precise fixed point control of the vessel dynamic positioning system and combining the unique function of the genetic algorithm such as sound selection of cross mutant objects and good memory for the characters of the particle swarm algorithm, a genetic particle swarm optimization algorithm is proposed in this paper and applied to the weight coefficient selection of the performance index weighting matrix of the optimal control. Through a marine multipurpose ship dynamic positioning point control simulation, the position of surge and sway as well as the angle of yaw gradually reach the desired value, and all output values are seen to converge and bounded. Compared with the results obtained in the traditional optimal control, the proposed algorithm is more effective and yields better optimization performance of the genetic particle swarm in the optimal control, and is thus more suitable for marine engineering applications.

关 键 词：船舶 动力定位系统 最优控制 遗传粒子群算法 权重系数 

分 类 号：U661.3[交通运输工程—船舶及航道工程]