水下拖曳体有关问题的初步探讨

本文介绍了通过海上实验及理论分析研制的拖曳体。同时对拖曳体的流体动力布局及其微调装置进行了阐述。该拖曳体经过多次海上实验结果表明：在拖速6—10节时，稳定性良好，下潜力大，阻力较小，可以控制机翼冲角的大小来增减下潜力，改变下潜深度，使其行驶于“锯齿形”的剖面上，随深度的变化自动记录温、盐度。如拖速8．5节，     

水下运载体定位理论模型及拖曳参数分析

通过分析连接水下运载体和信号浮标的缆绳的水下形态,提出一种将浮标的位置向水下延伸进行定位的方法。本方法比声学GPS定位系统更具经济性。并对拖曳系统某些参数进行了分析,得出了有益的结论。     

深海拖曳系统水下控制技术研究

深海拖曳系统是人类开发利用海洋的一项重要手段。在众多深海探测装备中,深海拖曳系统以探测面积广、作业效率高、操控性能好等优势而得到了广泛应用。针对深海拖曳系统水下控制技术,从控制手段和控制策略2个方面对其发展历程、国内外研究现状等进行梳理,分析对比不同控制手段的优缺点,并对深海拖曳系统水下控制技术未来的研究方向进行展望,以期为深海拖曳系统的应用与发展提供参考。     

水下拖体姿态角自适应控制器设计

水下拖曳系统在工作过程中拖体的俯仰角控制一直是水下拖体姿态控制的重要环节,设计了一种基于 RBF 神经网络的水下拖体直接自适应控制器,在闭环系统中利用 RBF 神经网络的局部无限逼近非线性函数的特性。将 RBF 神经网络的输出代替水下拖体动力学模型中的非线性不确定项,配合传统的 PD 控制器, 无需预先离线学习,在线学习更新神经网络权值,控制律和神经网络权值更新律经 Lyapunov 定理证明为稳定, 跟踪误差收敛到 0,通过计算机仿真比较该控制器与传统 PD 控制器的控制效果。     

基于模糊神经网络的多变量系统的解耦方法

针对多变量、强耦合、纯迟延系统,提出一种模糊神经网络的解耦方法,结合遗传算法、将多变量系统解耦成单变量系统。传统解耦方法对于非线性系统、变结构系统以及耦合关系和耦合强度随时间和负载变化的复杂系统经常无能为力,而这种综合了模糊逻辑和神经网络优势的解耦方法,由于具有非线性和自学习能力,使其解耦性能不受影响,弥补了传统解耦方法的缺陷,对复杂系统有着较好的解耦能力。且该方法不需要建立精确的数学模型,易于实现。文章最后通过仿真实验验证了该模型的解耦效果。     

拖曳式多参数剖面测量系统水动力与控制性能研究述评

综述了拖曳式多参数剖面测量系统水动力学研究的最新进展。对拖曳缆绳、拖曳体以及将两者耦合起来所构成的整个系统水动力及控制理论进行述评,分析了各种理论的优缺点从中简要地描述了拖曳式多参数剖面测量系统水动力研究的发展概要。     

拖曳式剖面探测拖体系统构成及运动控制试验

拖曳式剖面探测拖体是一种能做波浪式轨迹运动的载体,可对海洋进行实时多参数剖面探测.阐述国内首台拖曳式剖面探测拖体的组成、功能及运动轨迹,并对运动轨迹控制进行实船试验研究.结果表明,拖曳式剖面探测拖体具有控制性能优良,实现传感器数据的实时采集.     

基于分布式控制力矩陀螺的水下航行器轨迹跟踪控制

基于控制力矩陀螺群(CMGs)的水下航行器具有低速或零速机动的能力。采用基于分布式CMGs的水下航行器方案,并研究其水平面的轨迹跟踪控制问题。通过全局微分同胚变换将非完全对称的动力学模型解耦成标准欠驱动控制模型,并根据简化的模型构建其轨迹跟踪的误差动力学模型,将轨迹跟踪控制问题转化为误差模型镇定问题。基于一种分流神经元模型和反步法设计了系统的轨迹跟踪控制律,该控制器不需要对任何虚拟控制输入进行求导计算,且能确保跟踪误差的最终一致有界性。仿真结果表明该控制器能够实现在不依赖动力学参数先验知识的情况下对光滑轨迹的有效跟踪。     

海洋磁力仪拖体入水深度试验与分析

拖体入水深度是水下拖曳作业的主要参数,其取决于拖体自身重量、拖缆长度和船速。通过G882TVG海洋磁力仪阵列的拖曳试验,分析了船速、配重、拖缆长度的相互关系,导出拖体入水深度计算模型,对类似设备的拖曳参数确定具有借鉴意义。     

Neural Network Based Robust Controller for Trajectory Tracking of Underwater Vehicles

A robust neural network controller (NNC) is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A two-layer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded (UUB) stabilities of tracking errors and NN weights are guaranteed through the proposed controller. An on-line NN weights tuning algorithm is also proposed. Good performances of the tracking control system are illustrated by the results of numerical simulations.     

立式翼型主体拖曳式水下潜器的设计及操纵性能

针对现有拖曳式水下潜器控制机构复杂、航向与姿态不容易稳定的缺陷,提出和设计了一种具有航向与姿态稳定的多自由度可控制拖曳式水下潜器样机。该样机主要由鱼雷状浮体、固定水平主翼、转角可控制襟翼、立式翼型主体等部分组成,潜器的深度控制通过控制襟翼的偏转来诱导固定水平主翼攻角的改变来实现;潜器的横荡运动操纵以通过控制两个作为转艏控制器的导管螺旋桨的转向与转速、诱导立式翼型主体产生诱导力矩使其产生横向偏转来进行。文中所提出和设计的样机具有运动过程中自我稳定能力强、航向稳定性好、控制机构简单并具有较高实用价值的特点。     

基于模糊神经网络(FNN)的赤潮预警预测研究

为研究各种理化因子与赤潮藻类浓度间的非线性对应规律和有效预测赤潮藻类浓度,构建了基于BP 算法的一个四层模糊神经网络模型。将模糊神经网络(FNN)技术引入赤潮预测研究,并与普通 BP 网络、RBF 网络的结果作比较,结果表明,该模型能够较好地反演出各种理化因子与夜光藻密度的非线性对应变化规律,有更好的预测功能。     

Stability Analysis on Speed Control System of Autonomous Underwater Vehicle

The stability of the motion control system is one of the decisive factors of the control quality for Autonomous Underwater Vehicle (AUV).The divergence of control,which the unstable system may be brought about,is fatal to the operation of AUV.The stability analysis of the PD and S-surface speed controllers based on the Lyapunov' s direct method is proposed in this paper.After decoupling the six degree-of-freedom (DOF) motions of the AUV,the axial dynamic behavior is discussed and the condition is deduced,in which the parameters selection within stability domain can guarantee the system asymptotically stable.The experimental results in a tank and on the sea have successfully verified the algorithm reliability,which can be served as a good reference for analyzing other AUV nonlinear control systems.     

智能水下机器人水声精确定位技术的研究

讨论了智能水下机器人水声定位技术,给出系统的组成、定位教学模型。论述了提高定位精度措施,最后给出试验测量结果。     

Experimental Study of a Command Governor Adaptive Depth Controller for an Unmanned Underwater Vehicle

Unmanned Underwater Vehicles (UUVs) are increasingly being used in advanced applications that require them to operate in tandem with human divers and around underwater infrastructure and other vehicles. These applications require precise control of the UUVs which is challenging due to the non-linear and time varying nature of the hydrodynamic forces, presence of external disturbances, uncertainties and unexpected changes that can occur within the UUV’s operating environment. Adaptive control has been identified as a promising solution to achieve desired control within such dynamic environments. Nevertheless, adaptive control in its basic form, such as Model Reference Adaptive Control (MRAC) has a trade-off between the adaptation rate and transient performance. Even though, higher adaptation rates produce better performance they can lead to instabilities and actuator fatigue due to high frequency oscillations in the control signal. Command Governor Adaptive Control (CGAC) is a possible solution to achieve better transient performance at low adaptation rates. In this study CGAC has been experimentally validated for depth control of a UUV, which is a unique challenge due to the unavailability of full state measurement and a greater thrust requirement. These in turn leads to additional noise from state estimation, time-delays from input noise filters, higher energy expenditure and susceptibility to saturation. Experimental results show that CGAC is more robust against noise and time-delays and has lower energy expenditure and thruster saturation. In addition, CGAC offers better tracking, disturbance rejection and tolerance to partial thruster failure compared to the MRAC.     

基于多源信息融合的水下航行器航迹测量方法

针对水下航行器航迹实时分析的需求,提出了一种基于多源信息融合的测量方法。通过传感器获得水下航行器状态信息,利用主成分分析方法将试验数据作为一变量,定义总体的各主成分,利用测量值与主成分的复相关关系,给出了各传感器的综合支持程度和数据融合公式,完成多源数据的融合。应用实例验证了该方法的有效性和精确性,能够提高试验数据的可信度。     

多种群并行进化神经网络的研究及应用

提出一种新的多种群并行遗传算法 (NMPGA) ,并将其作为多层前馈神经网络(MFNNs)的学习算法 ,从而形成一类新的 MFNN模型——多种群并行进化神经网络(MPENNs)。首先 ,对一给定的网络结构 ,随机产生一初始权重的集合 ,这个集合实际上对应着一组具有相同结构但不同权重的神经网络。然后 ,采用 NMPGA对 MFNNs的权重进行进化。最后 ,性能最好的网络被选作目标问题的解。在 NMPGA算法中 ,作者采用浮点数编码来克服传统二进制编码的精度不足问题 ,并设计了专门的杂交算子和变异算子来增强算法性能。实验结果表明 ,MPENNs能成功解决异或问题、三元奇偶问题及成品烟的感官质量评价问题。     

Adaptive Neural Network Control with Control Allocation for A Manned Submersible in Deep Sea

This paper thoroughly studies a control system with control allocation for a manned submersible in deep sea being developed in China.The proposed control system consists of a neural-network-based direct adaptive controller and a dynamic control allocation module.A control energy cost function is used as the optimization criteria of the control allocation module,and weighted pseudo-inverse is used to find the solution of the control allocation problem.In the presence of bounded unknown disturbance and neural networks approximation error,stability of the closed-loop control system of manned submersible is proved with Lyaponov theory.The feasibility and validity of the proposed control system is further verified through experiments conducted on a semi-physical simulation platform for the manned submersible in deep sea.