水下智能潜器的神经网络运动控制

本文介绍一种基于神经网络的水下智能潜器的运动控制方法，该方法通过在线学习，融控制与滤波为一体。计算机仿真与水池实验验证表明，该方法的控制与滤波性能良好，对环境的学习与适应能力强。该方法事实上可用于一般动力系统的控制。     

连云港近岸海域物理自净能力研究及水质预测——Ⅲ.西大堤建成后对物质输运的影响

在连云港近岸海域计算潮流场基础上建立拉格朗日余流模型,并对连云港市两大堤建成前后的拉格朗日余流变化进行了分析,且选择有代表性的排污口进行了数值跟踪。     

珠江三角洲及珠江口海区的空气微生物含量

１９９４年６，７月，调查了广州、深圳、珠海及珠江口海区的空气微生物粒子沉降量状况。发现空气微生物粒子总量、真菌粒子量、真菌粒子数／总菌粒子数百分比在三市中不一致。这三种数据一般是陆上的高于海上。随着距陆的增加，都市影响的减弱和海面的开阔，空气微生物含量呈减少之势，显示出海洋新鲜空气对陆上污染空气的调节、净化作用。文中还分析了空气微生物有关参数与气温、风力或相对湿度间的相关关系。     

沿海与内陆地下卤水对比分析

分析对比我国滨海与内陆地下卤水的分布、成因及其水文地球化学特征，指出不同成因的地下卤水具有不同的水质．     

自主式水下机械手系统研究

本文从水下机械手的总体设计，关节直流电机的驱动和调速，手臂上声纳的水下搜索与跟踪，机械手系统的智能控制和电源等几个方面，介绍了有关自主式水下机械手系统的研究，可为研制自主式水下作业系统提供参考     

疏浚物倾倒区海水水质级别的模糊综合评价

应用模糊综合评价方法对大连臭水套海域疏浚物预选倾倒区的水质状况进行了评价，得到了有益的结论。     

Expanded adaptive fuzzy sliding mode controller using expert knowledge and fuzzy basis function expansion for UFV depth control

Generally, the underwater flight vehicle (UFV) depth control system operates with the following problems: it is a multi-input multi-output (MIMO) system, it requires robustness, a continuous control input, and further, it has the speed dependency of controller parameters. To solve these problems, an expanded adaptive fuzzy sliding mode controller (EAFSMC), which is based on the decomposition method designed by using an expert knowledge and the decoupled sub-controllers and composition method designed by using the fuzzy basis function expansions (FBFEs), is proposed. To verify the performance of the EAFSMC, the depth control of UFV in various operating conditions is performed. Simulation results show that the EAFSMC solves all problems experienced in the UFV depth control system online.     

诊断台风异常路径的三层权重控制点方案

本方案采用850、700、500hPa三个层次的环境平均流场作控制点法。综合考虑这些层次上的引导气流在不同时、空域中的相对重要性。试验结果表明,其效果比单一取某层作引导为佳。且对用来预测台风的未来24小时路径是否异常具有一定的参考价值。     

Robust and adaptive path following for underactuated autonomous underwater vehicles

This paper proposes a nonlinear robust adaptive control strategy to force a six degrees of freedom underactuated underwater vehicle with only four actuators to follow a predefined path at a desired speed despite of the presence of environmental disturbances and vehicle’s unknown physical parameters. The proposed controller is designed using Lyapunov’s direct method, the popular backstepping and parameter projection techniques. The closed loop path following errors can be made arbitrarily small. Interestingly, it is shown that our developed control strategy is easily extendible to situations of practical importance such as parking and point-to-point navigation. Numerical simulations are provided to illustrate the effectiveness of the proposed methodology.