Skip to main content
SpringerLink
Log in

A Generalized Recursive Identification Algorithm Compensated by Orthogonal Weighted Kernel for Tracking Time-Variant Systems

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

The main purpose of this paper is to introduce a generalized recursive identification algorithm compensated by orthogonal weighted kernel. The performance index of the novel proposed algorithm considers information about both the estimation error and first time derivative of estimation error. The estimation process is implemented in a recursive sliding window scheme. In addition, linear combinations of sine and cosine basis functions, which lead to Fourier orthogonal series, are employed as a kernel to approximate the time-varying parameters as continuous functions in each sliding window. Properties of the proposed algorithm are extended to both least squares estimator and instrumental variable estimator; thus, it is applicable to systems with correlated and uncorrelated noise. Simulation results demonstrate the efficiency of the proposed algorithm for accurate and online tracking of unknown parameters’ trend.

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

Similar content being viewed by others

References

  1. M.Z. Ali Bhotto, A. Antoniou, Robust recursive least-squares adaptive-filtering algorithm for impulsive-noise environments. IEEE Signal Process. Lett. 18(3), 185–188 (2011)

    Google Scholar 

  2. W. Allafi, K. Uddin, C. Zhang, R. Mazuir Raja Ahsan Sha, J. Marco, On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model. Appl. Energy 204, 497–508 (2017)

    Google Scholar 

  3. M. Ayati, A. Khaki-Sedigh, Adaptive control of nonlinear in parameters chaotic system via Lyapunov exponents placement. Chaos Solitons Fractals 41(4), 1980–1986 (2009)

    MATH  Google Scholar 

  4. M. Ayati, H. Khaloozadeh, A stable adaptive synchronization scheme for uncertain chaotic systems via observer. Chaos Solitons Fractals 42(4), 2473–2483 (2009)

    MathSciNet  MATH  Google Scholar 

  5. V.A. Bavdekar, A.P. Deshpande, S.C. Patwardhan, Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter. J. Process Control 21(4), 585–601 (2011)

    Google Scholar 

  6. M. Brunot, A. Janot, P.C. Young, F. Carrillo, An improved instrumental variable method for industrial robot model identification. Control Eng. Pract. 74, 107–117 (2018)

    MATH  Google Scholar 

  7. Z. Cen, P. Kubiak, I. Belharouak, Online parameter estimation/tracking for lithium-ion battery RC model, in Int. Renew. Sustain. Energy Conf. (2016)

  8. F. Ding, X. Wang, Q. Chen, Y. Xiao, Recursive least squares parameter estimation for a class of output nonlinear systems based on the model decomposition. Circuits Syst. Signal Process. 35(9), 3323–3338 (2016)

    MathSciNet  MATH  Google Scholar 

  9. T. Feng, L. Yang, X. Zhao, H. Zhang, J. Qiang, Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction. J. Power Sources 281, 192–203 (2015)

    Google Scholar 

  10. H. Garnier, Direct continuous-time approaches to system identification. Overview and benefits for practical applications. Eur. J. Control 24, 50–62 (2015)

    MathSciNet  MATH  Google Scholar 

  11. M. Gilson, What has instrumental variable method to offer for system identification? IFAC-PapersOnLine 28(1), 354–359 (2015)

    Google Scholar 

  12. G. Gopalratnam, J.S.J.R. Raol, Modelling and Parameter Estimation of Dynamic Systems (Institution of Engineering and Technology, London, 2004)

    MATH  Google Scholar 

  13. R. Havangi, Joint parameter and state estimation based on marginal particle filter and particle swarm optimization. Circuits Syst. Signal Process. 37(8), 3558–3575 (2018)

    MathSciNet  MATH  Google Scholar 

  14. M.R. Homaeinezhad, I. Tahbaz-Zadeh Moghaddam, Z. Khakpour, H. Naseri, Short-time linear quadratic form technique for estimating fast-varying parameters in feedback loops. Asian J. Control 17(6), 2289–2302 (2015)

    MathSciNet  MATH  Google Scholar 

  15. S. Hong, C. Lee, F. Borrelli, J.K. Hedrick, A novel approach for vehicle inertial parameter identification using a dual Kalman filter. IEEE Trans. Intell. Transp. Syst. 16(1), 151–161 (2014)

    Google Scholar 

  16. B. Kovačević, Z. Banjac, I.K. Kovačević, Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors. EURASIP J. Adv. Signal Process. 1, 2016 (2016)

    Google Scholar 

  17. M. Lagraoui, S. Doubabi, A. Rachid, SOC estimation of lithium-ion battery using Kalman filter and Luenberger observer: a comparative study, in Proc. 2014 Int. Renew. Sustain. Energy Conf. IRSEC 2014, pp. 636–641 (2014)

  18. W. Li, L. Liang, W. Liu, X. Wu, State of charge estimation of lithium-ion batteries using a discrete-time nonlinear observer. IEEE Trans. Ind. Electron. 64(11), 8557–8565 (2017)

    Google Scholar 

  19. L. Ljung, System Identification: Theory for the User (Prentice Hall, Upper Saddle River, 1999)

    MATH  Google Scholar 

  20. L. Ljung, Experiments with identification of continuous time models. IFAC Proc. 42(10), 1175–1180 (2009)

    Google Scholar 

  21. J. Marzbanrad, S. Jamali Shakhlavi, I. Tahbaz-zadeh Moghaddam, A. Afkar, Biomechanical modeling of a seated human body exposed to vertical and horizontal vibration using genetic algorithms. J. Vib. Eng. Technol. 6(5), 417–426 (2018)

    Google Scholar 

  22. J. Marzbanrad, I. Tahbaz-zadeh Moghaddam, Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade. Veh. Syst. Dyn. 54(9), 1291–1316 (2016)

    Google Scholar 

  23. Q. Ouyang, J. Chen, F. Wang, H. Su, Nonlinear observer design for the state of charge of lithium-ion batteries. IFAC Proc. Vol. (IFAC-PapersOnline) 47(3), 2794–2799 (2014)

    Google Scholar 

  24. P. Pramod, Z. Zhang, R. Mitra, S. Paul, R. Islam, J. Kleinau, Impact of parameter estimation errors on feedforward current control of permanent magnet synchronous motors. In: IEEE Transportation Electrification Conference and Expo (ITEC) (2016)

  25. M. Rahnavard, M. Ayati, M.R. Hairi Yazdi, M. Mousavi, Finite time estimation of actuator faults, states, and aerodynamic load of a realistic wind turbine. Renew. Energy 130, 256–267 (2019)

    Google Scholar 

  26. A. Ramezani, B. Safarinejadian, A modified fractional-order unscented Kalman filter for nonlinear fractional-order systems. Circuits Syst. Signal Process. 37(9), 3756–3784 (2018)

    MathSciNet  MATH  Google Scholar 

  27. S. Rhode, F. Bleimund, F. Gauterin, Recursive generalized total least squares with noise covariance estimation. IFAC Proc. 19, 4637–4643 (2014)

    Google Scholar 

  28. J. Schorsch, L.D. Couto, M. Kinnaert, SOC and SOH estimation for Li-ion battery based on an equivalent hydraulic model. Part II: SOH power fade estimation, in Proc. Am. Control Conf., pp. 4029–4034 (2016)

  29. T. Soderstrom, P. Stoica, System Identification. Prentice-Hall International, Hemel Hempstead, U.K. (1989)

  30. T. Soderstrom, P. Stoica, Instrumental variable methods for system identification. Circuits Syst. Signal Process. 21(1), 1–9 (2002)

    MathSciNet  MATH  Google Scholar 

  31. A. Taghavipour, R. Masoudi, N.L. Azad, J. McPhee, Control-relevant parameter estimation application to a model-based PHEV power management system. Optim. Control Appl. Methods 38(6), 1148–1167 (2017)

    MATH  Google Scholar 

  32. X. Tang, X. Mao, J. Lin, B. Koch, Li-ion battery parameter estimation for state of charge, in Proc. 2011 Am. Control Conf., pp. 941–946 (2011)

  33. Z. Wei, K.J. Tseng, N. Wai, T.M. Lim, M. Skyllas-Kazacos, Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery. J. Power Sources 332, 389–398 (2016)

    Google Scholar 

  34. P.C. Young, Refined instrumental variable estimation: maximum likelihood optimization of a unified Box–Jenkins model. Automatica 52, 35–46 (2015)

    MathSciNet  MATH  Google Scholar 

  35. Y. Yu, H. Zhao, R.C. de Lamare, Y. Zakharov, L. Lu, Robust distributed diffusion recursive least squares algorithms with side information for adaptive networks. IEEE Trans. Signal Process. 67(6), 1566–1581 (2019)

    MathSciNet  MATH  Google Scholar 

  36. Q. Zhong, F. Zhong, J. Cheng, H. Li, S. Zhong, State of charge estimation of lithium-ion batteries using fractional order sliding mode observer. ISA Trans. 66, 448–459 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Iman Tahbaz-zadeh Moghaddam & Moosa Ayati

  2. Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Amir Taghavipour

Authors
  1. Iman Tahbaz-zadeh Moghaddam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Moosa Ayati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir Taghavipour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Moosa Ayati.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tahbaz-zadeh Moghaddam, I., Ayati, M. & Taghavipour, A. A Generalized Recursive Identification Algorithm Compensated by Orthogonal Weighted Kernel for Tracking Time-Variant Systems. Circuits Syst Signal Process 39, 4903–4929 (2020). https://doi.org/10.1007/s00034-020-01394-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-020-01394-x

Keywords

Search

Navigation