摘要
基于BP神经网络控制方法设计了一种适用于自升式海洋平台的智能控制系统。为了验证该控制系统的有效性,以墨西哥湾海域某典型深水自升式海洋平台为原型,根据动力相似准则按照1∶40的相似比设计海洋平台试验模型,采用磁流变阻尼器作为实施该智能控制方法的控制器,并对其主要参数进行了设计。在此基础上,在波浪水池中分别对有、无安装控制系统的自升式海洋平台模型进行了多工况的波浪试验,通过对比安装控制系统前后平台结构的响应幅度,研究了控制系统的振动控制效果。试验结果表明:基于BP神经网络的磁流变阻尼智能控制系统能够有效地控制自升式海洋平台结构的动力响应,且控制效果稳定。
An intelligent control system which is applicable to deepwater jack-up platform was designed based on BP neural network. In order to study the control effect of this control system, a typical deep water jack-up platform in Mexico Gulf was taken as the prototype, and the test model was designed based on dynamical similarity criterion with a scale of 1：40. Magnetorheological Fluid Damper （ MR- FD） was selected as the intelligent control device, and its main parameters were designed. Furthermore, wave experiments under several different load cases were conducted in a wave pool respectively. Comparing the vibration response of the platform model with and without control system, the control effect was studied. Experimental results show that a MRFD intelligent control system based on BP neural network can reduce the vibration of the platform effectively and stably.
出处
《海洋工程》
CSCD
北大核心
2013年第2期19-27,共9页
The Ocean Engineering
基金
国家自然科学基金资助项目(51079065)
江苏省科技支撑计划资助项目(BE2010159)
江苏省普通高校研究生科研创新计划资助项目(CXZZ11_0278)
关键词
自升式平台
BP神经网络
智能控制
磁流变阻尼器
模型试验
jack-up platform
BP neural network
intelligent control
Magnetorheological Fluid Damper（MRFD）
model experiment
