Robust Fault Diagnosis for quadrotor uavs using Adaptive Thau observer

Zhaohui Cen, Hassan Noura, Tri Bagus Susilo, Younes Al Younes

Research output: Contribution to journalArticle

42 Citations (Scopus)

Abstract

A robust Fault Diagnosis (FD) scheme for a real quadrotor Unmanned Aerial Vehicle (UAV) is proposed in this paper. Firstly, a novel Adaptive Thau observer (ATO) is developed to estimate the quadrotor system states and build a set of offset residuals to indicate actuators' faults. Based on these residuals, some rules of Fault Diagnosis (FD) are designed to detect and isolate the faults as well as estimate the fault offset parameters. Secondly, a synthetic robust optimization scheme is presented to improve Fault Estimation (FE) accuracies, three key issues include modeling uncertainties, and magnitude order unbalances as well as noises are addressed. Finally, a typical fault of rotors is simulated and injected into one of four rotors of the quadrotor, and experiments for the FD scheme have been carried out. Unlike former research works on the FD schemes for quadrotors, our proposed FD scheme based on the ATO can not only detect and isolate the failed actuators, but also estimate the fault severities. Regardless of roughness of the real flying data, the FD results still have sufficient FE accuracies.

Original languageEnglish
Pages (from-to)573-588
Number of pages16
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume73
Issue number1-4
DOIs
Publication statusPublished - Jan 2014

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Keywords

  • Adaptive Thau observer
  • Fault detection
  • Isolation and estimation
  • Quadrotor
  • Robustness

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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