Quadrotors are increasingly used in the evolving field of aerial robotics for their agility and mechanical simplicity. However, inherent uncertainties, such as aerodynamic effects coupled with quadrotors' operation in dynamically changing environments, pose significant challenges for traditional, nominal model-based control designs. We propose a multi-task meta-learning method called Encoder-Prototype-Decoder (EPD), which has the advantage of effectively balancing shared and distinctive representations across diverse training tasks. Subsequently, we integrate the EPD model into a model predictive control problem (Proto-MPC) to enhance the quadrotor's ability to adapt and operate across a spectrum of dynamically changing tasks with an efficient online implementation. We validate the proposed method in simulations, which demonstrates Proto-MPC's robust performance in trajectory tracking of a quadrotor being subject to static and spatially varying side winds.

翻译：摘要：四旋翼飞行器因其敏捷性和机械简单性，在正在发展中的空中机器人领域得到日益广泛的应用。然而，固有不确定性（例如气动效应与四旋翼飞行器在动态变化环境中的运行相互耦合）对传统的基于标称模型的控制设计构成了重大挑战。我们提出了一种称为编码器-原型-解码器（EPD）的多任务元学习方法，该方法具有在多样训练任务中有效平衡共享表征与独特表征的优势。随后，我们将EPD模型集成到模型预测控制问题（Proto-MPC）中，以通过高效的在线实现增强四旋翼飞行器在一系列动态变化任务中适应和运行的能力。我们通过仿真验证了所提方法，结果表明Proto-MPC在承受静态和空间变化侧风的四旋翼飞行器轨迹跟踪任务中展现出鲁棒性能。