基于深度学习的四旋翼无人机地面效应补偿降落控制设计

针对四旋翼无人机在降落控制过程中地面效应对控制性能有较大影响的问题,在地面效应复杂,难以建立机理模型的约束下,提出一种基于深度学习的新型非线性鲁棒控制策略.利用深度神经网络的学习能力,建立无人机降落过程中未知地面效应的补偿模型;结合super-twisting控制设计,实现对降落过程中未知地面效应的快速抑制和无人机降落...

Robust landing controller design for quadrotor unmanned aerial vehicle ground effects compensation via deep learning

This paper proposes a control strategy based on deep learning for a quadrotor to suppress the unknown ground effects during the landing procedure. Due to the complexity of the grounds effects, it is very difficult to obtain the accurate dynamic model. To solve this issue, we set up a compensation model for the ground effects in the landing procedure by using the learning ability of the deep neural network (DNN). Then the super--twisting method is combined with the DNN to formulate a nonlinear robust adaptive landing control strategy which is able to suppress the ground effects and drive the quadrotor to its desired landing point accurately. The Lyapunov based stability analysis and the spectral normalization are employed to prove the stability of the closed loop system, and finite--time convergence of landing error is also achieved. Real-time flight experimental results are included to show the good landing control performance of the proposed control strategy.