Skip to main content
SpringerLink
Log in

Fault estimation for nonlinear uncertain systems utilizing neural network-based robust iterative learning scheme

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

In this paper, a novel neural network-based robust iterative learning fault estimation scheme is proposed to address the problem of fault modeling and estimation in nonlinear manipulator systems with disturbance and parameter uncertainties. The aim is to enhance the rapidity, efficiency, and accuracy of fault estimation. Firstly, the modeling for flexible manipulator control system is constructed as a preparation of iterative learning fault estimation observer design. Then, the neural network model is constructed to optimize the gain parameters of iterative learning fault estimator to approximate nonlinear uncertainties. Additionally, a \(H\mathrm {\infty }\) robust technique is used to suppress fault variation rate and disturbance, which enhances the speed of estimation and reduces the impact of disturbance. So that the estimated fault can rapidly and accurately track the actual fault over the whole time interval and iterations. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed neural network-based robust iterative learning scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

References

  1. Cheng, H., Wu, J., Huang, D., Liu, Y., Wang, Q.: Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment. ISA Trans. 177, 210–220 (2021)

    Article  Google Scholar 

  2. Kong, X., Yang, Z., Luo, J., Li, H., Yang, X.: Extraction of reduced fault subspace based on KDICA and its application in fault diagnosis. IEEE Trans. Instrum. Meas. 71, 3505212 (2022)

    Article  Google Scholar 

  3. Chen, B.S., Lee, M.Y., Lin, T.H.: Zhang, W: Robust state/fault estimation and fault-tolerant control in discrete-time ts fuzzy systems: an embedded smoothing signal model approach. IEEE Trans. Cybernetics. 52(7), 6886–6900 (2021)

    Article  Google Scholar 

  4. Han, W., Long, P., Xu, B.: Fault detection for uncertain polynomial fuzzy systems using \( H_ {-}/L_ {\infty } \) observer and ellipsoidal analysis. IEEE Trans. Fuzzy Syst. 31(4), 1113–1123 (2022)

    Article  Google Scholar 

  5. Xia, J., Jiang, B., Zhang, K.: Resilient observer design of sensor fault estimation for discrete-time multi-agent systems: a distributed approach. Int. J. Robust Nonlin. 31(18), 9604–9618 (2021)

    Article  MathSciNet  Google Scholar 

  6. Niu, Y., Gao, M., Sheng, L.: Fault-tolerant state estimation for stochastic systems over sensor networks with intermittent sensor faults. Appl. Math. Comput. 414, 126723 (2022)

    MathSciNet  Google Scholar 

  7. Zhang, K., Jiang, B., Chen, M., Yan, X.G.: Distributed fault estimation and fault-tolerant control of interconnected systems. IEEE Trans. Cybern. 51(3), 1230–1240 (2019)

    Article  Google Scholar 

  8. Jia, Q., Li, Y., Liu, Z.: A new fault isolation approach based on propagated nonnegative matrix factorizations. J Intell Fuzzy Syst. 43(4), 4271–4284 (2022)

    Article  Google Scholar 

  9. Xu, L., Li, T., Jia, Z.: Song, G: Fault-tolerant control for stochastic distribution systems based on iterative learning observer. Electron. Opt. Control. 28(1), 19–23 (2021)

    Google Scholar 

  10. Mousavi, S.M.M., Ramezani, A.: A new sliding mode observer design for fault estimation in a class of nonlinear fractional-order systems based on the super-twisting algorithm. J. Control Autom. Electr. Syst. 32(6), 1531–1539 (2021)

    Article  Google Scholar 

  11. Chen, L., Liu, M., Shi, Y., Zhang, H., Zhao, E.: Adaptive fault estimation for unmanned surface vessels with a neural network observer approach. IEEE Trans. Circuits Syst. I Reg. Pap. 68(1), 416–425 (2021)

    Article  MathSciNet  Google Scholar 

  12. Gassara, H., Elloumi, M., Naifar, O., Ben Makhlouf, A.: Robust fault estimation for polynomial systems with time delay via a polynomial adaptive observer. Trans. Inst. Meas. Control 45(5), 955–962 (2023). https://doi.org/10.1177/01423312221124977

    Article  Google Scholar 

  13. Yang, X., Ruan, X., Geng, Y.: Iterative learning fault-tolerant control for discrete-time nonlinear systems subject to stochastic actuator faults. Trans. Inst. Meas. Control 44(10), 2012–2023 (2022)

    Article  Google Scholar 

  14. Gui, Y., Jia, Q., Li, H., Cheng, Y.: Reconfigurable fault-tolerant control for spacecraft formation flying based on iterative learning algorithms. Appl. Sci. 12(5), 2485 (2022)

    Article  Google Scholar 

  15. Li, L., Yao, L., Wang, H., Gao, Z.: Iterative learning fault diagnosis and fault tolerant control for stochastic repetitive systems with Brownian motion. ISA. Trans. 121, 171–179 (2022)

    Article  Google Scholar 

  16. He, T., Wu, Z.: Iterative learning disturbance observer based attitude stabilization of flexible spacecraft subject to complex disturbances and measurement noises. IEEE-CAA J. Autom. 18(9), 1576–1587 (2021)

    MathSciNet  Google Scholar 

  17. Arimoto, S., Kawamura, S., Miyazaki, F.: Bettering operation of Robots by learning. J. Robot. Syst. 1(2), 123–140 (1984)

    Article  Google Scholar 

  18. Feng, L., Xu, S., Chai, Y., Yang, Z., Zhang, K.: Iterative learning scheme to design intermittent fault estimators for nonlinear systems with parameter uncertainties and measurement noise. Int. J. Adapt. Control Signal Process. 32(7), 994–1009 (2018)

    Article  MathSciNet  Google Scholar 

  19. Feng, L., Xu, S., Chai, Y., Yang, Z., Zhang, K.: A nonrepetitive fault estimation design via iterative learning scheme for nonlinear systems with iteration-dependent references. Neural Comput. Appl. 34(7), 5169–5179 (2022)

    Article  Google Scholar 

  20. Zhang, J., Li, X., Zhou, H.: Fault diagnosis for control actuator based on iterative learning observer. Tech. Autom. Appl. 41(7), 1–5 (2022)

    Google Scholar 

  21. Chen, Z., Han, L., Hou, Y.: Fault detection and estimation based on adaptive iterative learning algorithm for nonlinear systems. Control Theory Appl. 37(4), 144–153 (2020)

    Google Scholar 

  22. Gao, J., Zhou, B., Zi, B., Qian, S., Zhao, P.: Kinematic uncertainty analysis of a cable-driven parallel robot based on an error transfer model. J. Mech. Robot. 14(5), 051008 (2022)

    Article  Google Scholar 

  23. Xiao, B., Cao, L., Xu, S., Liu, L.: Robust tracking control of robot manipulators with actuator faults and joint velocity measurement uncertainty. IEEE/ASME Trans. Mechatron. 25(3), 1354–1365 (2020)

    Article  Google Scholar 

  24. Chen, Z., Han, L., Hou, Y.: Robust fault estimation and isolation for a descriptor LPV system with disturbance. J. Frankl. Inst. 358(2), 1635–1655 (2021)

    Article  MathSciNet  Google Scholar 

  25. Li, X.J., Wang, N.: Data-driven fault estimation and control for unknown discrete-time systems via multiobjective optimization method. IEEE Trans. Cybern. 52(5), 3289–3301 (2022)

    Article  Google Scholar 

  26. Luo, M., Fang, H., Li, Y., Bai, Y., Chen, J., Wei, Y.: Distributed bobust fault estimation using relative measurements for leader-follower multiagent systems. IEEE Trans. Cybern. 51(9), 4707–4715 (2021)

    Article  Google Scholar 

  27. Pei, X.: Construction and application of talent evaluation model based on nonlinear hierarchical optimization neural network. Comput. Intel. Neurosc. 2022, 6834253 (2022)

    Article  Google Scholar 

  28. Kaya, E.: A comprehensive comparison of the performance of metaheuristic algorithms in neural network training for nonlinear system identification. Mathematics 10(9), 1611 (2022)

    Article  Google Scholar 

  29. Ma, L., Wang, M.: Neural network adaptive compensation control based on the backstepping method for nonlinear systems with time-varying delays. Int. J. Syst. Sci. 54(6), 1196–1212 (2023)

  30. Gong, X., Fu, W., Bian, X., Fei, J.: Adaptive backstepping terminal sliding mode control of nonlinear system using fuzzy neural structure. Mathematics 11(5), 1094 (2023)

  31. Benrabah, M., Kara, K., AitSahed, O., Hadjili, M.L.: Adaptive Fourier series neural network PID controller. Int. J. Control Autom. 19(10), 3388–3399 (2021)

    Article  Google Scholar 

  32. Assawinchaichote, W., Pongfai, J., Zhang, H., Shi, Y.: Optimal design of a nonlinear control system based on new deterministic neural network scheduling. Inf. Sci. 609, 339–352 (2022)

    Article  Google Scholar 

  33. Wang, Y., Xie, L., De Souza, C.E.: Robust control of a class of uncertain nonlinear systems. Syst. Control Lett. 19(2), 139–149 (1992)

    Article  MathSciNet  Google Scholar 

  34. Xie, M., Ding, D., Wei, G., Yi, X.: H-infinity fusion estimation of time-delayed nonlinear systems with energy constraints: the finite-horizon case. Nonlinear Dyn. 107(3), 2583–2598 (2022)

    Article  Google Scholar 

  35. Pazera, M., Witczak, M.: A novel adaptive sensor fault estimation algorithm in robust fault diagnosis. Sensors 22, 9638 (2022)

  36. Li, L., Ding, S.X., Peng, X.: Optimal observer-based fault detection and estimation approaches for T -S fuzzy systems. IEEE Trans. Fuzzy Syst. 30(2), 579–590 (2022)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61374134), in part by the Natural Science Foundation of Henan Province (No. 232300421149) and in part by the Postgraduate Cultivating Innovation and Quality Improvement Action Plan of Henan University (No. SYLYC2022081).

Funding

This work was supported in part by the National Natural Science Foundation of China (No. 61374134), in part by the Natural Science Foundation of Henan Province (No. 232300421149) and in part by the Postgraduate Cultivat ing Innovation and Quality Improvement Action Plan of Henan University (No. SYLYC2022081).

Author information

Authors and Affiliations

  1. School of Computer and Information Engineering, Henan University, Kaifeng, 475000, China

    Zhengquan Chen & Ruirui Huang

  2. School of Artificial Intelligence, Henan University, Zhengzhou, 450001, China

    Jiulong Ma, Jinjin Wang & Yandong Hou

Authors
  1. Zhengquan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruirui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiulong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinjin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yandong Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yandong Hou.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Huang, R., Ma, J. et al. Fault estimation for nonlinear uncertain systems utilizing neural network-based robust iterative learning scheme. Nonlinear Dyn 112, 6421–6438 (2024). https://doi.org/10.1007/s11071-024-09397-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-024-09397-y

Keywords

Mathematics Subject Classification

Search

Navigation