In this paper, we study the conventional and learning-based control approaches for multi-rotor platforms, with and without the presence of an actuated “tail” appendage. A comprehensive experimental comparison between the proven control-theoretic approaches and more recent learning-based ones is one of the contributions. Furthermore, an actuated tail appendage is considered as a deviation from the typical multi-rotor morphology, complicating the control problem but promising some useful applications. Our study also explores, as another contribution, the impact of such an actuated tail on the overall position control for both the conventional as well as learning-based controllers. For the conventional control part, we used a multi-loop architecture where the inner loop regulates the attitude while the outer loop controls the position of the platform. For the learning controller, a multi-layer neural network architecture is used to learn a nonlinear state-feedback controller. To improve the learning and generalization performance of this controller, we adopted a curricular learning approach which gradually increases the difficulty of training samples. For the experiments, a planar bi-rotor platform is modeled in a 2D simulation environment. The planar model avoids mathematical complications while preserving the main attributes of the problem making the results more useful. We observe that both types of controllers achieve reasonable control performance and can solve the position control task. However, neither one shows a clear advantage over the other. The learning-based controller is not intuitive and the system suffers from long training times. The architecture of the multi-loop controller is handcrafted (not required for the learning-based controller) but provides a guaranteed stable behavior.

在本文中，我们研究了多旋翼平台的传统和基于学习的控制方法，无论是否存在驱动的“尾部”附件。已证明的控制理论方法与最近的基于学习的方法之间的综合实验比较是贡献之一。此外，驱动尾翼被认为是与典型的多旋翼形态的偏差，使控制问题复杂化，但有望获得一些有用的应用。作为另一个贡献，我们的研究还探讨了这种驱动尾部对传统控制器和基于学习的控制器的整体位置控制的影响。对于常规控制部分，我们使用了多回路架构，其中内回路调节姿态，而外回路控制平台的位置。对于学习控制器，使用多层神经网络架构来学习非线性状态反馈控制器。为了提高该控制器的学习和泛化性能，我们采用了逐渐增加训练样本难度的课程学习方法。对于实验，在二维仿真环境中对平面双转子平台进行建模。平面模型避免了数学上的复杂性，同时保留了问题的主要属性，使结果更有用。我们观察到两种类型的控制器都实现了合理的控制性能并且可以解决位置控制任务。然而，没有一个显示出明显的优势。基于学习的控制器不直观，并且系统训练时间长。