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首页> 外文会议>25th International Conference on Offshore Mechanics and Arctic Engineering 2006(OMAE2006) vol.2: Ocean Engineering; Polar and Arctic Sciences and Technology >HYDRODYNAMIC AND CONTROL ANALYSIS OF A SELF-CONTROLLABLE UNDERWATER TOWED VEHICLE
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HYDRODYNAMIC AND CONTROL ANALYSIS OF A SELF-CONTROLLABLE UNDERWATER TOWED VEHICLE

机译：自控水下拖曳车辆的水动力与控制分析

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摘要

The hydrodynamic and control performances of a self-stable controllable underwater towed vehicle developed by South China University of Technology under different depth trajectory control operations are analyzed by means of a proposed hydrodynamic numerical model. The model is established based on LMBP algorithm of neural network theory. Training samples for the neural network model are provided from the experimental data of the vehicle prototype towing experiments conducted in a large-scale ship model towing tank under the manipulation of a depressing wing installed in the vehicle. After the LMBP model is established, a depth trajectory control system for the towed vehicle is designed in order to accomplish vehicle trajectory control. This system is mainly composed of tow parts: a neural network identifier based on genetic algorithm and a fuzzy neural network controller based on genetic algorithm simulated annealing. Hydrodynamic performances of the vehicle under various control operations can then be numerically simulated with the proposed LMBP model and the depth trajectory control system of the towed vehicle. In numerical simulation of trajectory control to the towed vehicle, deflection of the vehicle's depressing wing is adjusted at every time step by the proposed control system to match the trajectory of the vehicle with a pre-designated one. The value of the deflection is taken as input parameter for the LMBP neural network model, trajectory and attitude behavior of the towed vehicle under the control manipulations can then be predicted by the LMBP model.
机译：通过提出的水动力数值模型，分析了华南理工大学研制的自稳定可控水下拖曳车在不同深度轨迹控制下的水动力性能。该模型基于神经网络理论的LMBP算法建立。神经网络模型的训练样本由在大型船模拖曳舱中进行的车辆原型拖曳实验的实验数据提供，这些实验是在安装在车辆中的压降机翼的操纵下进行的。建立LMBP模型后，设计了拖曳车辆的深度轨迹控制系统，以完成车辆轨迹控制。该系统主要由两部分组成：基于遗传算法的神经网络识别器和基于遗传算法模拟退火的模糊神经网络控制器。然后，可以使用所提出的LMBP模型和被牵引车辆的深度轨迹控制系统，对车辆在各种控制操作下的流体力学性能进行数值模拟。在对拖曳车辆进行轨迹控制的数值模拟中，所提出的控制系统会在每个时间步调整车辆压降机翼的偏转，以使车辆的轨迹与预先指定的轨迹相匹配。将挠度值作为LMBP神经网络模型的输入参数，然后可以通过LMBP模型预测在控制操作下的牵引车的轨迹和姿态行为。

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