论文标题
神经释放：使用学习的互动，分散的近距离多电流控制
Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions
论文作者
论文摘要
在本文中，我们提出了神经损害，这是一种非线性分散稳定控制器，用于多旋转群的近距离飞行。由于多电动器之间的复杂空气动力学相互作用（例如从高级车辆到较低的车辆）之间的复杂空气动力学相互作用，因此近距离控制具有挑战性。常规方法通常无法正确捕获这些相互作用的效果，从而导致控制器必须保持车辆之间的安全距离较大，因此无法近距离飞行。我们的方法结合了名义动力学模型与正则化置换不变的深神经网络（DNN），该模型准确地学习了高阶多车辆相互作用。我们使用学习模型设计稳定的非线性跟踪控制器。实验结果表明，所提出的控制器的表现明显优于基线非线性跟踪控制器，其最大最差的案例高度跟踪误差四倍。我们还从经验上证明了我们学到的模型概括到更大的群体大小的能力。
In this paper, we present Neural-Swarm, a nonlinear decentralized stable controller for close-proximity flight of multirotor swarms. Close-proximity control is challenging due to the complex aerodynamic interaction effects between multirotors, such as downwash from higher vehicles to lower ones. Conventional methods often fail to properly capture these interaction effects, resulting in controllers that must maintain large safety distances between vehicles, and thus are not capable of close-proximity flight. Our approach combines a nominal dynamics model with a regularized permutation-invariant Deep Neural Network (DNN) that accurately learns the high-order multi-vehicle interactions. We design a stable nonlinear tracking controller using the learned model. Experimental results demonstrate that the proposed controller significantly outperforms a baseline nonlinear tracking controller with up to four times smaller worst-case height tracking errors. We also empirically demonstrate the ability of our learned model to generalize to larger swarm sizes.
