Nonlinear Actuator Fault Detection for Small-Scale UASs

  • Authors:

  • X. Yang;L. Mejias;F. Gonzalez;M. Warren;B. Upcroft;B. Arain

  • Affiliations:

  • Australian Research Center for Aerospace Automation (ARCAA) and Queensland University, of Technology (QUT), Brisbane, Australia;Australian Research Center for Aerospace Automation (ARCAA) and Queensland University, of Technology (QUT), Brisbane, Australia;Australian Research Center for Aerospace Automation (ARCAA) and Queensland University, of Technology (QUT), Brisbane, Australia;Australian Research Center for Aerospace Automation (ARCAA) and Queensland University, of Technology (QUT), Brisbane, Australia;School of Electrical Engineering and Computer Science, QUT, Brisbane, Australia;Australian Research Center for Aerospace Automation (ARCAA) and Queensland University, of Technology (QUT), Brisbane, Australia

  • Venue:
  • Journal of Intelligent and Robotic Systems
  • Year:
  • 2014

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Abstract

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating corresponding velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.