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Effect of the damping and excitation on the identification of the hydrodynamic parameters for an underwater robotic vehicle

机译:阻尼和激励对水下机器人车辆水动力参数识别的影响

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The main objective of this work is to investigate the effects of the damping level as well as different excitation forms on the overall prediction of the hydrodynamic parameters in the equations describing the coupled heave and pitch motions for an Underwater Robotic Vehicle (URV) sailing near the sea surface in random waves. The response of an underwater vehicle heaving and pitching in random waves having wide-band and narrow-band spectra are generated. The RDLRNNT technique is used to identify the hydrodynamic parameters in the equations. The technique is based on a combination of a multiple linear regression algorithm and a neural networks technique. The combination of the classical parametric identification techniques and the neural networks technique provides robust results and does not require a large amount of computer time. The identification technique would be particularly useful in identifying the parameters for both moderately and lightly damped motions under the action of unknown excitations effected by a realistic sea. It is shown that the developed technique produces reliable results for the parameters in the equations describing the coupled heave and pitch motions for a URV.
机译:这项工作的主要目的是研究阻尼水平以及不同激励形式对流体动力学参数总体预测的影响,该方程描述了在水下航行的水下机器人车辆(URV)的升沉和俯仰运动耦合。海面在随机波。产生水下机器人在具有宽带和窄带光谱的随机波中起伏和俯仰的响应。 RDLRNNT技术用于识别方程中的流体动力学参数。该技术基于多重线性回归算法和神经网络技术的组合。经典参数识别技术和神经网络技术的结合提供了可靠的结果,并且不需要大量的计算机时间。该识别技术在识别由现实海域引起的未知激发作用下的适度和轻度阻尼运动的参数时特别有用。结果表明,所开发的技术针对描述URV的波动和俯仰运动耦合的方程式中的参数产生了可靠的结果。

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