论文标题
具有不确定动态的不确定车辆的基于在线学习的轨迹跟踪
Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics
论文作者
论文摘要
由于空中和水下车辆以及纳米卫星的量增加，近年来，近年来的车辆不足引起了很多关注。这些车辆的轨迹跟踪控制是不断增加的应用域范围的重要方面。但是，外部干扰和内部动力学的一部分通常是未知或非常耗时的模型。为了克服这个问题，我们提出了一种跟踪控制法，用于使用基于在线学习的甲骨文来预测未知动力学的僵化动态。我们表明，高斯过程模型对甲骨文的作用特别感兴趣。提出的方法保证了有限的跟踪误差，在明确给出界限的情况下，概率很高。一个数字示例突出了拟议的控制法的有效性。
Underactuated vehicles have gained much attention in the recent years due to the increasing amount of aerial and underwater vehicles as well as nanosatellites. Trajectory tracking control of these vehicles is a substantial aspect for an increasing range of application domains. However, external disturbances and parts of the internal dynamics are often unknown or very time-consuming to model. To overcome this issue, we present a tracking control law for underactuated rigid-body dynamics using an online learning-based oracle for the prediction of the unknown dynamics. We show that Gaussian process models are of particular interest for the role of the oracle. The presented approach guarantees a bounded tracking error with high probability where the bound is explicitly given. A numerical example highlights the effectiveness of the proposed control law.
