基于遗传和小脑模型算法的新型航向控制器  

Design of New Ship Course Controller Based on Genetic and Cerebellar Model Algorithm

作　　者：陈进涛[1] 王仁强[1] 杜加宝[1] 

机构地区：[1]江苏海事职业技术学院,南京211170

出　　处：《中国航海》2014年第3期10-14,35,共6页Navigation of China

基　　金：江苏省教育科学"十二五"规划重点课题(B-a/2011/03/008);江苏省高校大学生实践探究训练计划课题(2012JSSPITP3772)

摘　　要：运用智能算法研发一种既能缩短航程又能降低能耗的新型船舶航向控制器。在分析船舶运动的非线性数学模型和普通PID控制算法的基础上,运用遗传算法优化整定常规的PID控制参数,以实现反馈控制并保证系统的稳定性;同时,运用小脑模型神经网络进行前馈非线性控制,以抑制源自船舶内外部的非线性扰动,最终确保系统的控制精度和响应速度。运用船舶操纵模拟器对新型航向控制器进行检验,结果表明其性能稳定,功能优越。In order to design a ship course controller good for reducing both sailing milage and power consumption, the non- nonlinear mathematical model of ship motion and the current ship course PID control algorithms are analyzed thoroughly; The PID parameters are optimized online with genetic algorithms（GA） to achieve superior feedback control and ensure sys- tem stability. Besides, CMAC neural network feed-forward nonlinear control is used to suppress internal and external dis- turbances for achieving control accuracy and response speed. The new ship course controller is verified on ship-maneuvering simulator. Simulation result proves that new ship course controller has superior performance and robustness.

关 键 词：船舶工程 CMAC神经网络 遗传算法 PID智能控制 航向控制器 

分 类 号：U664.82[交通运输工程—船舶及航道工程]