The nonlinear observer-based fault diagnosis method for the high altitude airship

Jichen Hu , Ming Zhu , Tian Chen

Electronic Research Archive ›› 2025, Vol. 33 ›› Issue (2) : 907 -930.

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Electronic Research Archive ›› 2025, Vol. 33 ›› Issue (2) :907 -930. DOI: 10.3934/era.2025041
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The nonlinear observer-based fault diagnosis method for the high altitude airship
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Abstract

We investigated an observer-based fault diagnosis method for the high altitude airship (HAA). The nonlinear kinematics and dynamics model, which considers model uncertainties and external disturbances, were obtained. The nonliear portion of the model was re-expressed to the linear parameter varying frame mathematically with an auxiliary design. Subsequently, in order to realize fault detection, isolation, and reconstruction, diverse types of robust were observers designed, respectively. Furthermore, all these mentioned observers were achieved via the linear matrix inequation framework. The effectiveness of the proposed method was verified by different fault cases in an HAA simulation system.

Keywords

the high altitude airship / fault diagnosis / nonlinear system / robust observer / linear parameter varying

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Jichen Hu, Ming Zhu, Tian Chen. The nonlinear observer-based fault diagnosis method for the high altitude airship. Electronic Research Archive, 2025, 33 (2) : 907-930 DOI:10.3934/era.2025041

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References

[1]

Z. Zuo, J. Song, Z. Zheng, Q. Han, A survey on modelling, control and challenges of stratospheric airships, Control Eng. Pract., 119 (2022), 104979. https://doi.org/10.1016/j.conengprac.2021.104979

[2]

T. Chen, M. Zhu, Z. Zheng, Asymmetric error-constrained path-following control of a stratospheric airship with disturbances and actuator saturation, Mech. Syst. Signal Process., 119 (2019), 501-522. https://doi.org/10.1016/j.ymssp.2018.10.003

[3]

J. Yuan, M. Zhu, X. Guo, W. Lou, Finite-time trajectory tracking control for a stratospheric airship with full-state constraint and disturbances, J. Franklin Inst., 358 (2021), 1499-1528. https://doi.org/10.1016/j.jfranklin.2020.12.010

[4]

J. Yuan, X. Guo, Z. Zheng, M. Zhu, H. Gou, Error-constrained fixed-time trajectory tracking control for a stratospheric airship with disturbances, Aerosp. Sci. Technol., 118 (2021), 107055. https://doi.org/10.1016/j.ast.2021.107055

[5]

S. Liang, S. Zhang, Y. Huang, X. Zheng, J. Cheng, S. Wu, Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions, ISA Trans., 126 (2022), 472-485. https://doi.org/10.1016/j.isatra.2021.07.043

[6]

T. Thanaraj, K. H. Low, B. F. Ng, Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems, ISA Trans., 138 (2023), 168-185. https://doi.org/10.1016/j.isatra.2023.02.026

[7]

J. J. Gertler, M. M. Kunwer, Optimal residual decoupling for robust fault diagnosis, Int. J. Control, 61 (1995), 395-421. https://doi.org/10.1080/00207179508921908

[8]

A. Khan, W. Xie, B. Zhang, L. Liu, A survey of interval observers design methods and implementation for uncertain systems, J. Franklin Inst., 358 (2021), 3077-3126. https://doi.org/10.1016/j.jfranklin.2021.01.041

[9]

S. Meng, F. Meng, F. Zhang, Q. Li, Y. Zhang, A. Zemouche, Observer design method for nonlinear generalized systems with nonlinear algebraic constraints with applications, Automatica, 162 (2024), 111512. https://doi.org/10.1016/j.automatica.2024.111512

[10]

F. Nemati, S. M. S. Hamami, A. Zemouche, A nonlinear observer-based approach to fault detection, isolation and estimation for satellite formation flight application, Automatica, 107 (2019), 474-482. https://doi.org/10.1016/j.automatica.2019.06.007

[11]

G. Zogopoulos-Papaliakos, K. J. Kyriakopoulos, An efficient approach for graph-based fault diagnosis in UAVs, J. Intell. Rob. Syst., 97 (2020), 553-576. https://doi.org/10.1007/s10846-019-01061-7

[12]

R. Falcón, H. Ríos, A. Dzul, A robust fault diagnosis for quad-rotors: A sliding-mode observer approach, IEEE/ASME Trans. Mechatron., 27 (2022), 4487-4496. https://doi.org/10.1109/TMECH.2022.3156854

[13]

L. Shang, G. Liu, Sensor and actuator fault detection and isolation for a high performance aircraft engine bleed air temperature control system, IEEE Trans. Control Syst. Technol., 19 (2011), 1260-1268. https://doi.org/10.1109/TCST.2010.2076353

[14]

Y. Wei, J. Qiu, P. Shi, L. Wu, A piecewise-markovian lyapunov approach to reliable output feedback control for fuzzy-affine systems with time-delays and actuator faults, IEEE Trans. Cybern., 48 (2018), 2723-2735. https://doi.org/10.1109/TCYB.2017.2749239

[15]

S. Li, H. Wang, A. Aitouche, Y. Tian, N. Christov, Actuator fault and disturbance estimation using the T-S fuzzy model, IFAC-PapersOnLine, 50 (2017), 15722-15727. https://doi.org/10.1016/j.ifacol.2017.08.2414

[16]

H. Ma, Y. Liu, T. Li, G. Yang, Nonlinear high-gain observer-based diagnosis and compensation for actuator and sensor faults in a quadrotor unmanned aerial vehicle, IEEE Trans. Ind. Inf., 15 (2019), 550-562. https://doi.org/10.1109/TII.2018.2865522

[17]

R. C. Avram, X. Zhang, J. Muse, Quadrotor actuator fault diagnosis and accommodation using nonlinear adaptive estimators, IEEE Trans. Control Syst. Technol., 25 (2017), 2219-2226. https://doi.org/10.1109/TCST.2016.2640941

[18]

W. Chua, J. C. L. Chan, C. P. Tan, E. K. P. Chong, S. Saha, Robust fault reconstruction for a class of nonlinear systems, Automatica, 113 (2020), 108718. https://doi.org/10.1016/j.automatica.2019.108718

[19]

F. Zhu, Y. Tang, Z. Wang, Interval-observer-based fault detection and isolation design for T-S fuzzy system based on zonotope analysis, IEEE Trans. Fuzzy Syst., 30 (2022), 945-955. https://doi.org/10.1109/TFUZZ.2021.3050854

[20]

M. Zhang, Z. Wang, Y. Wang, J. H. Park, Z. Ji, Interval observer filtering-based fault diagnosis method for linear discrete-time systems with dual uncertainties, J. Franklin Inst., 359 (2022), 1626-1648. https://doi.org/10.1016/j.jfranklin.2021.11.018

[21]

S. Li, H. Wang, A. Aitouche, N. Christov, Sliding mode observer design for fault and disturbance estimation using takagi-sugeno model, Eur. J. Control, 44 (2018), 114-122. https://doi.org/10.1016/j.ejcon.2018.09.006

[22]

A. N. Zhirabok, A. E. Shumsky, A. V. Zuev, Fault diagnosis in linear systems via sliding mode observers, Int. J. control, 94 (2021), 327-335. https://doi.org/10.1080/00207179.2019.1590738

[23]

R. Zhou, J. Li, Z. Yang, Y. Xu, Z. Yu, Y. Zhang, Fractional-order sliding-mode fault-tolerant control of unmanned airship against actuator faults, IFAC-PapersOnLine, 55 (2022), 617-622. https://doi.org/10.1016/j.ifacol.2022.07.196

[24]

H. Hamdi, M. Rodrigues, C. Mechmeche, N. B. Braiek, Fault diagnosis based on sliding mode observer for LPV descriptor systems, Asian J. Control, 21 (2019), 89-98. https://doi.org/10.1002/asjc.2022

[25]

T. Chen, M. Zhu, Z. Zheng, Adaptive path following control of a stratospheric airship with full-state constraint and actuator saturation, Aerosp. Sci. Technol., 95 (2019), 105457. https://doi.org/10.1016/j.ast.2019.105457

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