Swarm aerial robots are required to maintain close proximity to successfully traverse narrow areas in cluttered environments. However, this movement is affected by the downwash effect generated from other quadrotors in the swarm. This aerodynamic effect is highly nonlinear and hard to describe through mathematical modeling. Additionally, the existence of the downwash disturbance can be predicted based on the states of neighboring quadrotors. If this prediction is considered, the control loop can proactively handle the disturbance, resulting in improved performance. To address these challenges, we propose an approach that integrates a Neural network Downwash Predictor with Nonlinear Model Predictive Control (NDP-NMPC). The neural network is trained with spectral normalization to ensure robustness and safety in uncollected cases. The predicted disturbances are then incorporated into the optimization scheme in NMPC, which enforces constraints to ensure that states and inputs remain within safe limits. We also design a quadrotor system, identify its parameters, and implement the proposed method on board. Finally, we conduct a prediction experiment to validate the safety and effectiveness of the network. In addition, a real-time trajectory tracking experiment is performed with the entire system, demonstrating a 75.37% reduction in tracking error in height under the downwash effect.

翻译：集群空中机器人在杂乱环境中穿越狭窄区域时需保持近距离飞行。然而，这种运动受到机群中其他四旋翼产生的下洗流效应影响。该空气动力学效应高度非线性且难以通过数学建模描述。此外，基于相邻四旋翼的状态可预测下洗流干扰的存在。若考虑这种预测，控制回路能够主动处理干扰，从而提升性能。针对这些挑战，我们提出了一种集成神经网络下洗流预测器与非线性模型预测控制（NDP-NMPC）的方法。神经网络采用谱归一化训练，以确保在未采集情况下的鲁棒性和安全性。随后，将预测的干扰纳入NMPC的优化框架中，通过约束确保状态与输入维持在安全范围内。我们还设计了一套四旋翼系统，辨识其参数，并在机载平台上实现了所提方法。最后，通过预测实验验证了网络的安全性与有效性。此外，基于完整系统的实时轨迹跟踪实验表明，在下洗流效应影响下，高度跟踪误差降低了75.37%。