论文标题
使用多输出高斯工艺对水下车辆的动态系统识别
Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes
论文作者
论文摘要
在这项研究中探索了水下车辆高斯工艺的非参数系统识别，目的是对自动驾驶水下车辆（AUV）动力学进行建模，但数据量较低。使用多输出高斯过程及其对未丢失AUV的动态系统建模而不会失去绑定输出之间的关系的能力。使用Remus 100 AUV的第一原理模型的模拟来捕获数据和验证多输出高斯过程的数据。本文还显示了对具有6个自由度（DOF）的AUV进行多输出高斯流程的度量和所需的程序。将多输出的高斯流程与复发性神经网络的流行技术进行了比较，表明，多输出高斯工艺能够超过具有高度耦合DOF的水下车辆中非参数动态系统识别的RNN，并带来了高度耦合的DOF，并带来了提供置信度测量的额外好处。
Non-parametric system identification with Gaussian Processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle (AUV) dynamics with low amount of data. Multi-output Gaussian processes and its aptitude to model the dynamic system of an underactuated AUV without losing the relationships between tied outputs is used. The simulation of a first-principles model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper. Multi-output Gaussian processes are compared with the popular technique of recurrent neural network show that Multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing a measurement of confidence.
