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A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

Rui Sun1, Qi Cheng2, Guanyu Wang3

  • 1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. rui.sun@nuaa.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a new Adaptive Neuron Fuzzy Inference System (ANFIS) method for detecting navigation sensor faults in Unmanned Aerial Vehicles (UAVs). The ANFIS approach ensures flight safety through accurate, real-time fault detection without needing predefined fault models.

Keywords:
adaptive neuron fuzzy inference systemdata-drivennavigation sensor fault detectiononline

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicles (UAVs) rely heavily on integrated navigation sensors for flight control and safety.
  • Timely and effective detection of navigation sensor faults is crucial for ensuring UAV operational integrity.
  • Existing fault detection methods often depend on predefined or modeled faults, limiting their adaptability.

Purpose of the Study:

  • To present a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS) approach for detecting on-board navigation sensor faults in UAVs.
  • To develop a fault detection system that combines online data training with an ANFIS decision system for enhanced reliability.
  • To enable real-time, model-free residual analysis for improved fault detection capabilities.

Main Methods:

  • Implementation of an Adaptive Neuron Fuzzy Inference System (ANFIS) for fault detection.
  • Integration of an online data training mechanism with the ANFIS decision system.
  • Utilizing Kalman Filter (KF) estimates for real-time, model-free residual analysis.

Main Results:

  • The proposed ANFIS-based method demonstrated effective detection of navigation sensor faults in UAVs.
  • The system achieved high accuracy and a low misdetection rate, suitable for real-time applications.
  • Experimental results validated the effectiveness and reliability of the novel fault detection approach.

Conclusions:

  • The developed ANFIS approach provides a robust and accurate solution for on-board navigation sensor fault detection in UAVs.
  • The combination of online training and KF residual analysis enables real-time, model-free fault identification.
  • This method significantly enhances UAV flight safety by ensuring timely and effective fault detection.