基于迁移学习的四旋翼无人机性能驱动的故障检测  

Performance-Driven Fault Detection for Quadrotor UAV Based on Transfer Learning

作　　者：薛山 李琳琳 乔梁[1,2] 丁梦龙 XUE shan;LI Linlin;QIAO Liang;DING Menglong(University of Science and Technology Beijing,School of Automation and Electrical Engineering,Beijing 100083,China;Key Laboratory of Knowledge Automation for Industrial Process,Ministry of Education,Beijing 100083,China)

机构地区：[1]北京科技大学自动化学院,北京100083 [2]工业过程知识自动化教育部重点实验室,北京100083

出　　处：《空间控制技术与应用》2023年第4期59-66,共8页Aerospace Control and Application

基　　金：国家自然科学基金资助项目(62073029、U21A20483和62003033)。

摘　　要：致力于解决四旋翼无人机的故障检测问题.考虑到无人机模型是一个非线性强耦合的模型,提出一种基于神经网络的性能驱动故障检测方法.然而,当无人机进入新的重力场时,已建立的故障检测系统无法适用.为了解决这个问题,进一步提出一种基于迁移学习的故障检测方法.通过子空间迁移方法和布雷格曼散度度量方式,将源域与目标域对齐,并实现了神经网络的参数迁移以及阈值设定.在四旋翼无人机系统中验证了本文所提出的方法的有效性.The fault detection for quadrotor unmanned aerial vehicle(UAV)is studied in this paper.Considering the UAV model is nonlinear and strongly coupled,a performance-driven fault detection method is proposed based on neural network.However,the established fault detection system cannot be applied when the UAV enters a new gravitational field.To deal with solve this problem,a fault detection method is proposed based on transfer learning.By means of subspace transfer method and Bregman divergence measurement method,the source domain and target domain are aligned,and the parameter transfer and threshold setting of neural network are realized.Finally,we verify the effectiveness of the proposed method in a four-rotor UAV system.

关 键 词：四旋翼无人机 故障检测 神经网络 迁移学习 子空间迁移 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]