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.
翻译:近年来,由于航空和水下飞行器以及超小型卫星的数量不断增加,未受活化的飞行器在最近几年中受到高度重视,这些飞行器的轨迹跟踪控制是越来越多的应用领域的一个重要方面,然而,外部扰动和内部动态的某些部分往往不为人所知,或非常耗费时间来模拟。为解决这一问题,我们提出了一个用于预测未知动态的在线基于学习的触角,对未受活性硬体动态进行跟踪的控制法。我们表明,高斯过程模型对甲骨文的作用特别感兴趣。提出的方法保证了在明确给出约束的情况下,有高度概率的捆绑跟踪错误。一个数字例子突出了拟议控制法的有效性。