Skip to main content
SpringerLink
Log in

Impact analysis of the multi-harmonic input splicing way based on the data-driven model

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

In response to the identification problem of nonlinear systems with harmonic input, this paper studies the identification effect of the different splicing way of the multi-harmonic input based model identification approach. In this method, the relationship between the input and output signals of the nonlinear system is represented by the Nonlinear Auto-Regressive model with exogenous inputs (NARX) model. Firstly, the modeling framework of the NARX model based on the multi-harmonic input method is introduced. Then the effect of the different splicing way contained sequence and period of splicing data on modeling results is researched and proved in theoretically. Finally, a case study is discussed to simultaneously validate that the different multi-harmonic signal splicing forms have no effect on the identification results.

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

Similar content being viewed by others

References

  1. Nelles O (2013) Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Berlin

    MATH  Google Scholar 

  2. Billings SA (1980) Identification of nonlinear systems–a survey. In: IEE Proceedings D (control theory and applications), IET, pp 272–285

  3. Keesman KJ (2011) System identification: an introduction. Springer, Berlin

    Book  Google Scholar 

  4. Liu H-P, Zhu Y-P, Luo Z, Han Q-K (2018) PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems. Front Mech Eng 13:390–400

    Article  Google Scholar 

  5. Ma Y, Luo Z, Liu H-P, Zhu Y-P (2018) The NRSF-SVM based method for nonlinear rotor bearing fault diagnosis. In: Chinese Control And Decision Conference (CCDC), IEEE, pp 3978–3983

  6. Qin Z-Y, Yan S-Z, Chu F-L (2011) Dynamic characteristics of launch vehicle and spacecraft connected by clamp band. J Sound Vib 330:2161–2173

    Article  Google Scholar 

  7. Mu J, Rees D, Liu G (2005) Advanced controller design for aircraft gas turbine engines. Control Eng Pract 13:1001–1015

    Article  Google Scholar 

  8. Zhang W, Li X, Jia X-D, Ma H, Luo Z, Li X (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement 152:107377

    Article  Google Scholar 

  9. Li X, Jia X-D, Zhang W, Ma H, Luo Z, Li X (2020) Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation. Neurocomputing 383:235–247

    Article  Google Scholar 

  10. Li X, Zhang W, Xu N-X, Ding Q (2019) Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places. IEEE Trans Industr Electron 67:6785–6794

    Article  Google Scholar 

  11. Elmarakbi A, Elkady M, MacIntyre J (2013) Numerical analysis of vehicle-to-vehicle impact using vehicle dynamics control systems for collision mitigation. Int J Dyn Control 1:172–191

    Article  Google Scholar 

  12. Guo Y, Guo L, Billings SA, Wei H-L (2015) An iterative orthogonal forward regression algorithm. Int J Syst Sci 46:776–789

    Article  MathSciNet  Google Scholar 

  13. Billings SA, Wei H-L (2005) The wavelet-NARMAX representation: a hybrid model structure combining polynomial models with multiresolution wavelet decompositions. Int J Syst Sci 36:137–152

    Article  MathSciNet  Google Scholar 

  14. Zhu Y-P, Lang Z-Q (2017) Design of nonlinear systems in the frequency domain: an output frequency response function-based approach. IEEE Trans Control Syst Technol 26:1358–1371

    Article  Google Scholar 

  15. Billings SA (2013) Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley, New York

    Book  Google Scholar 

  16. Ruano AE, Fleming PJ, Teixeira C, RodrıGuez-Vázquez K, Fonseca CM (2003) Nonlinear identification of aircraft gas-turbine dynamics. Neurocomputing 55:551–579

    Article  Google Scholar 

  17. Ge SS, Zhang J, Lee TH (2004) Adaptive MNN control for a class of non-affine NARMAX systems with disturbances. Syst Control Lett 53:1–12

    Article  MathSciNet  Google Scholar 

  18. Mao K, Billings SA (1997) Algorithms for minimal model structure detection in nonlinear dynamic system identification. Int J Control 68:311–330

    Article  MathSciNet  Google Scholar 

  19. Farina M, Piroddi L (2012) Identification of polynomial input/output recursive models with simulation error minimisation methods. Int J Syst Sci 43:319–333

    Article  MathSciNet  Google Scholar 

  20. Araújo ÍB, Guimarães JP, Fontes AI, Linhares LL, Martins AM, Araújo FM (2019) NARX Model Identification Using Correntropy Criterion in the Presence of Non-Gaussian Noise. J Control Autom Electr Syst 30:453–464

    Article  Google Scholar 

  21. Solares JA, Wei H-L (2015) Nonlinear model structure detection and parameter estimation using a novel bagging method based on distance correlation metric. Nonlinear Dyn 82:201–215

    Article  Google Scholar 

  22. Billings SA, Wei H-L (2008) An adaptive orthogonal search algorithm for model subset selection and non-linear system identification. Int J Control 81:714–724

    Article  MathSciNet  Google Scholar 

  23. Chen S, Wang X, Harris CJ (2007) NARX-based nonlinear system identification using orthogonal least squares basis hunting. IEEE Trans Control Syst Technol 16:78–84

    Article  Google Scholar 

  24. Billings SA, Chen S, Korenberg M (1989) Identification of MIMO non-linear systems using a forward-regression orthogonal estimator. Int J Control 49:2157–2189

    Article  Google Scholar 

  25. Shariff HM, Marzaki MH, Tajjudin M, Rahiman MHF. (2013) System identification for steam distillation pilot plant: Comparison between linear and nonlinear models. In: IEEE 3rd International conference on system engineering and technology, IEEE, pp 263–268

  26. Liu H, Song X (2015) Nonlinear system identification based on NARX network. In: 10th Asian Control Conference (ASCC), IEEE, pp 1–6

  27. Wei H-L, Billings SA (2007) A comparative study on global wavelet and polynomial models for non-linear regime-switching systems. Int J Model Ident Control 2:273–282

    Article  Google Scholar 

  28. Zhu Y-P, Lang Z-Q, Kawanishi Y, Kohiyama M (2020) Semi-actively Implemented Non-linear Damping for Building Isolation Under Seismic Loadings. Front Built Environ 6:19

    Article  Google Scholar 

  29. Ge X-B, Luo Z, Ma Y, Liu H-P, Zhu Y-P (2019) A novel data-driven model based parameter estimation of nonlinear systems. J Sound Vib 453:188–200

    Article  Google Scholar 

  30. Ma Y, Liu H-P, Zhu Y-P, Wang F, Luo Z (2017) The NARX model-based system identification on nonlinear, rotor-bearing systems. Appl Sci 7:911

    Article  Google Scholar 

  31. Zhu Y-P, Lang Z-Q (2020) A new convergence analysis for the Volterra series representation of nonlinear systems. Automatica 111:108599

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This project is supported by the National Natural Science Foundation of China (Grant Nos. 11872148, U1908217); the Fundamental Research Funds for the Central Universities of China (Grant Nos. N2003012, N2003013, N170308028, N180703018).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, People’s Republic of China

    Yue Qiu, Zhong Luo, Xiaobiao Ge & Yi Gao

  2. Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education, Northeastern University, Shenyang, 110819, People’s Republic of China

    Yue Qiu, Zhong Luo, Xiaobiao Ge, Yunpeng Zhu & Yi Gao

  3. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Zhong Luo

  4. Department of Automatic Control and System Engineering, University of Sheffield, Sheffield, S13JD, UK

    Yunpeng Zhu

Authors
  1. Yue Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaobiao Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunpeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong Luo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, Y., Luo, Z., Ge, X. et al. Impact analysis of the multi-harmonic input splicing way based on the data-driven model. Int. J. Dynam. Control 8, 1181–1188 (2020). https://doi.org/10.1007/s40435-020-00700-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-020-00700-4

Keywords

Search

Navigation