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首页> 外文期刊>Proceedings of the Institute of Marine Engineering, Science and Technology >Modelling the yaw dynamics of an uninhabited surface vehicle for navigation and control systems design
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Modelling the yaw dynamics of an uninhabited surface vehicle for navigation and control systems design

机译：为无人驾驶水上飞机的偏航动力学建模，以进行导航和控制系统设计

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摘要

In many navigation and control systems a key element in both their software architectures is a model which describes the dynamic characteristics of the given vessel.Thus, to ensure uninhabited surface vehicles are capable of successfully completing missions, it is essential that the marine control systems designer has available bona fide dynamic models that are accurate and possess good prediction capabilities. Since hydrodynamic modelling is usually very expensive, time-consuming and requires the use of specialist equipment in the form of a tank testing facility, it was considered more appropriate to model the vehicle yaw dynamics using Black-box identification techniques. In this paper five new models of the yaw dynamics of the Springer uninhabited surface vehicle are developed and presented. Two of the models that are based on auto-regressive with exogenous variable and auto-regressive moving average with exogenous variable techniques are linear in composition and expressed in their state space formats; while the others are nonlinear and described by multi-layer perception, radial basis function and recursive neural networks. In all cases, the models were optimised using a steady-state genetic algorithm.The models are compared using a Mean Square Error criterion and residual correlation analysis. From the evaluation undertaken, it is shown that the yaw dynamic model based on the recursive neural network outperformed the others in terms of predictability and exactness.Therefore, it is concluded the recursive neural network model is the best candidate for navigation and control systems design studies involving the Springer vehicle.
机译：在许多导航和控制系统中，这两种软件体系结构中的关键要素都是描述给定船舶动态特性的模型。因此，要确保无人水面车辆能够成功完成任务，船舶控制系统设计人员至关重要有可用的真实动态模型，这些模型准确且具有良好的预测能力。由于流体动力学建模通常非常昂贵，耗时，并且需要使用坦克测试设施形式的专业设备，因此认为使用黑匣子识别技术对车辆偏航动力学建模更为合适。在本文中，开发并提出了五个新的Springer无人水面载具偏航动力学模型。基于外生变量的自回归和外生变量技术的自回归移动平均的两个模型在组成上是线性的，并以它们的状态空间格式表示。其他则是非线性的，并通过多层感知，径向基函数和递归神经网络进行描述。在所有情况下，均使用稳态遗传算法对模型进行优化，并使用均方误差准则和残差相关分析对模型进行比较。从进行的评估中可以看出，基于递归神经网络的偏航动力学模型在可预测性和准确性方面都优于其他模型，因此可以得出结论，递归神经网络模型是导航和控制系统设计研究的最佳选择涉及施普林格的车辆。

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    SK Sharma; R Sutton;

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    Marine and Industrial Dynamic Analysis Research Group, Advanced Engineering Systems and Interactions, School of Marine Science and Engineering, Plymouth University, UK;

    Marine and Industrial Dynamic Analysis Research Group, Advanced Engineering Systems and Interactions, School of Marine Science and Engineering, Plymouth University, UK;

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