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A novel fault diagnosis in sensors of quadrotor unmanned aerial vehicle

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Abstract

Rapid faults diagnosis and isolation in flight control systems is vital to evade undesirable effects on the environment, humans as well as on the system itself. In this research, a new strategy based on an online dynamical system is proposed for sensors fault diagnosis and isolation of quadrotor unmanned aerial vehicle (UAV). In the presented strategy, the multi-layer neural network (MLNN) is adopted as an observer for sensor faults diagnosis of quadrotor unmanned aerial vehicle (UAV). For the improvement of faults detection accuracy and effectiveness, two new adaptive weight-updating approaches are used in this study. Lyapunov function theory-based approaches are proposed for updating the learning rate parameters of a multi-layer neural network (MLNN). The key purpose of utilizing these approaches is to attain the global minima for nonlinear functions without enhancement of computational power to enhance the accuracy of faults diagnosis and isolation. The proposed fault detection (FD) approaches are applied to quadrotor unmanned aerial vehicle (UAV), the simulated results demonstrate that the proposed approaches are having the capability of rapid faults diagnosis and isolation of sensors compared to the conventional neural network and the techniques used in the literature (Chen et al. in IEEE Trans Syst Man Cybern Syst Hum 46:260–270, 2016a, b, c, d; Payam et al. in J Intell Robot Syst 90:473–484, 2018).

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Funding

I am grateful to all the reviewers for reviewing my paper. This research is supported by the National Natural Science Foundation of China (61773245, 61806113, 61873048, 91848206, 61973200, 62103245 and 62073199), the Natural Science Foundation of Shandong Province (ZR2020MF095), Taishan Scholarship Construction Engineering, and 2019 Science and Technology Project of West Coast New Area of Qingdao (2019-32).

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Muhammad Taimoor, Xiao Lu & Chunyang Sheng

  2. Department of Electrical Technology, Government Polytechnical Institute, Karak, 27200, Pakistan

    Hamid Maqsood

Authors
  1. Muhammad Taimoor
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  2. Xiao Lu
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  3. Hamid Maqsood
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  4. Chunyang Sheng
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Corresponding author

Correspondence to Xiao Lu.

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Appendices

Appendix A

See Table 3.

Table 3 Parameter specifications of quadrotor
Full size table

Appendix B

Nomenclature

Parameter

Definition

\(p,q,r\)

Angular velocity about x, y, z axes [\(rad/s\)]

\({I}_{x}, {I}_{y}, {I}_{z}\)

Moment of inertia, [\(kg.{m}^{2}\)]

\(\varphi (.)\)

Neural Network activation function

\(w\)

NN weight parameter

ƞ

NN learning rate parameter

\(\varphi , \theta ,\psi \)

Euler Angles [\(rad or deg\)]

\(u, v,w\)

Velocities [m/sec] along \(x, y \mathrm{and} z\) axes

\({M}_{p}, {M}_{q},{M}_{r}\)

Roll, pitch and yaw moments

\({c}_{t}\)

Anti-torque coefficient

\({c}_{f}\)

Thrust force coefficient

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Taimoor, M., Lu, X., Maqsood, H. et al. A novel fault diagnosis in sensors of quadrotor unmanned aerial vehicle. J Ambient Intell Human Comput 14, 14081–14099 (2023). https://doi.org/10.1007/s12652-022-04113-3

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