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Optimal trim control of a high-speed craft by trim tabs/interceptors Part I: Pitch and surge coupled dynamic modelling using sea trial data

机译:通过纵倾片/拦截器对高速船进行最佳纵倾控制第一部分:使用海试数据的俯仰和喘振耦合动态建模

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摘要

A pitch and surge coupled dynamic model of a high-speed craft is not available for dynamic trim control applications in the literature. The existing fluid-structure interaction models of a high-speed craft are not adequate for simulations and control applications, since they require a great deal of computation time, for example more than 20-40 s depending on a vessel particulars. Hence, in this work, we aimed to obtain a dynamic model of a high-speed craft for surge and pitch motions. Then the obtained model will be utilized to design an automatic controller which adjust the command signal on a high-speed craft to increase fuel efficiency, safety and comfort of passengers in a vessel. The coupled pitch and surge motion of a high-speed craft with trim tabs/interceptors was modelled by using full scale sea trial data. The linear parametric modelling using System Identification (SI) Methods and Artificial Neural Network (ANN) modelling were carried out and the comparisons of both the training and validation results are given. High correlation coefficients and low average values of absolute errors in surge and pitch dynamics were obtained by using ANN Method. The ANN model can be improved for further control designs on a marine vessel's operations.
机译:在文献中,高速船的俯仰和喘振耦合动态模型不适用于动态微调控制应用。高速船的现有流体-结构相互作用模型不足以用于仿真和控制应用,因为它们需要大量的计算时间,例如,取决于船只的具体情况,将超过20-40 s。因此,在这项工作中,我们旨在获得用于浪涌和俯仰运动的高速船的动力学模型。然后,将使用获得的模型来设计自动控制器,该控制器可调节高速船上的命令信号,以提高燃油效率,船上乘客的安全性和舒适性。带有装饰片/拦截器的高速船的俯仰和喘振运动是通过使用完整的海试数据建模的。进行了使用系统识别(SI)方法和人工神经网络(ANN)建模的线性参数化建模,并给出了训练和验证结果的比较。通过使用ANN方法,可获得高相关系数,并且喘振和俯仰动力学的绝对误差平均值较低。可以改进ANN模型,以进一步进行船舶操作的控制设计。

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