In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing.

翻译：本文报告了对三种流行的无人水面艇(USV)在线系统辨识方法的实验评估，这些方法以集成方式实现：可认证稳定的浅层循环神经网络(RNN)、自适应辨识(AID)和递归最小二乘(RLS)。这些算法部署在八艘USV上，总计进行了30小时的在线估计。在线训练过程中，RNN的损失函数被扩展，增加了对违反RNN在压缩稳定性意义下稳定充分条件的惩罚项。此外，我们还描述了一种高效计算RNN平衡点并分类这些点附近相关稳定性特性的方法。实验发现，AID方法在在线预测场景中具有最低的平均绝对误差，但加权集成方法在离线处理中误差更低。