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
作者:
作者单位:

天津大学电气自动化与信息工程学院

作者简介:

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中图分类号:

TP273

基金项目:

国家重点研发计划资助(2018YFB1403900), 国家自然科学基金(91748121, 90916004)


{Robust Landing Controller Design for a Quadrotor Unmanned Aerial Vehcile Ground Effects Compensation via Deep Learning
Author:
Affiliation:

School of Electrical and Information Engineering, TIanjin University

Fund Project:

National Key R&D Program of China (2018YFB1403900), Natural Science Foundation of China (91748121, 90916004)

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    摘要:

    本文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,提出了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,建立了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,实现了对降落过程中未知地面效应的快速抑制和无人机降落的精确控制. 通过Lyapunov分析法和谱归一化法,证明了降落过程中闭环系统的稳定性和无人机位置误差的有限时间收敛特性.实时飞行实验结果表明,本文中提出的控制策略取得了较好的控制效果.

    Abstract:

    In this paper, we propose a novel 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.

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历史
  • 收稿日期:2020-02-24
  • 最后修改日期:2020-05-12
  • 录用日期:2020-05-29
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