An Adaptive Threshold Neural-Network Scheme for Rotorcraft UAV Sensor Failure Diagnosis

  • Authors:

  • Juntong Qi;Xingang Zhao;Zhe Jiang;Jianda Han

  • Affiliations:

  • Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), 114# Nanta Street, Shenyang, P.R. China and Graduate School, Chinese Academy of Sciences, Beijing, P.R. Ch ...;Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), 114# Nanta Street, Shenyang, P.R. China and Graduate School, Chinese Academy of Sciences, Beijing, P.R. Ch ...;Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), 114# Nanta Street, Shenyang, P.R. China and Graduate School, Chinese Academy of Sciences, Beijing, P.R. Ch ...;Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), 114# Nanta Street, Shenyang, P.R. China

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents an adaptive threshold neural-network scheme for Rotorcraft Unmanned Aerial Vehicle (RUAV) sensor failure diagnosis. The approach based on adaptive threshold has the advantages of better detection and identification ability compared with traditional neural-network-based scheme. In this paper, the proposed scheme is demonstrated using the model of a RUAV and the results show that the adaptive threshold neural-network method is an effective tool for sensor fault detection of a RUAV.