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Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters

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Abstract

A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori information of the system is proposed to model the DC–DC converters. A parsimonious model with fewer parameters is used to represent the system, and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data. Compared with traditional data-driven methods for DC–DC converters, the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity. Moreover, compared with the traditional full-parameter constrained subspace approach, the proposed algorithm can accurately estimate the system parameters with a smaller variance. The experimental results on a DC–DC synchronous buck converter verify the effectiveness and superiority of the proposed method.

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References

  1. Kobaku, T., Patwardhan, S. C., & Agarwal, V. (2017). Experimental evaluation of internal model control scheme on a dc-dc boost converter exhibiting nonminimum phase behavior. IEEE Transactions on Power Electronics, 32(11), 8880–8891. https://doi.org/10.1109/TPEL.2017.2648888

    Article  Google Scholar 

  2. Renaudineau, H., Martin, J.-P., Nahid-Mobarakeh, B., & Pierfederici, S. (2015). Dc-dc converters dynamic modeling with state observer-based parameter estimation. IEEE Transactions on Power Electronics, 30(6), 3356–3363. https://doi.org/10.1109/TPEL.2014.2334363

    Article  Google Scholar 

  3. Algreer, M., Armstrong, M., & Giaouris, D. (2012). Active online system identification of switch mode dc-dc power converter based on efficient recursive dcd-iir adaptive filter. IEEE Transactions on Power Electronics, 27(11), 4425–4435. https://doi.org/10.1109/TPEL.2012.2190754

    Article  Google Scholar 

  4. Zhang, X., Min, R., Lyu, D., Zhang, D., Wang, Y., & Gu, Y. (2019). Current tracking delay effect minimization for digital peak current mode control of dc-dc boost converter. IEEE Transactions on Power Electronics, 34(12), 12384–12395. https://doi.org/10.1109/TPEL.2019.2905864

    Article  Google Scholar 

  5. Ahmad, S., & Ali, A. (2019). Active disturbance rejection control of dc-dc boost converter: A review with modifications for improved performance. IET Power Electronics, 12(8), 2095–2107. https://doi.org/10.1049/iet-pel.2018.5767

    Article  Google Scholar 

  6. Kanzian, M., Gietler, H., Unterrieder, C., Agostinelli, M., Lunglmayr, M., & Huemer, M. (2019). Low-complexity state-space-based system identification and controller auto-tuning method for multi-phase dc-dc converters. IEEE Transactions on Industry Applications, 55(2), 2076–2087. https://doi.org/10.1109/TIA.2018.2878687

    Article  Google Scholar 

  7. Rygg, A., & Molinas, M. (2017). Apparent impedance analysis: A small-signal method for stability analysis of power electronic-based systems. IEEE Journal of Emerging and Selected Topics in Power Electronics, 5(4), 1474–1486. https://doi.org/10.1109/JESTPE.2017.2729596

    Article  Google Scholar 

  8. Li, B. X., & Low, K. S. (2016). Low sampling rate online parameters monitoring of dc-dc converters for predictive-maintenance using biogeography-based optimization. IEEE Transactions on Power Electronics, 31(4), 2870–2879. https://doi.org/10.1109/TPEL.2015.2472459

    Article  Google Scholar 

  9. Gietler, H., Kanzian, M., Unterrieder, C., Berger, A., Priewasser, R., Huemer, M., & Zangl, H. (2020). on-time mismatch based system identification technique for buck converters. IEEE Transactions on Industrial Electronics, 67(9), 7898–7908. https://doi.org/10.1109/TIE.2019.2939982

    Article  Google Scholar 

  10. Francés, A., Asensi, R., & Uceda, J. (2019). Blackbox polytopic model with dynamic weighting functions for dc-dc converters. IEEE Access, 7, 160263–160273. https://doi.org/10.1109/ACCESS.2019.2950983

    Article  Google Scholar 

  11. Singer, S., & Erickson, R. W. (1992). Canonical modeling of power processing circuits based on the popi concept. IEEE Transactions on Power Electronics, 7(1), 37–43. https://doi.org/10.1109/63.124575

    Article  Google Scholar 

  12. Chen, F., Garnier, H., Deng, Q., Kazimierczuk, M. K., & Zhuan, X. (2020). Control-oriented modeling of wireless power transfer systems with phase-shift control. IEEE Transactions on Power Electronics, 35(2), 2119–2134. https://doi.org/10.1109/TPEL.2019.2920863

    Article  Google Scholar 

  13. Hou, J., Su, H., Yu, C., Chen, F., & Li, P. (2023). Bias-correction errors-in-variables hammerstein model identification. IEEE Transactions on Industrial Electronics, 70(7), 7268–7279. https://doi.org/10.1109/TIE.2022.3199931

    Article  Google Scholar 

  14. Hou, J., Su, H., Yu, C., Chen, F., Li, P., Xie, H., & Li, T. (2023). Consistent subspace identification of errors-in-variables hammerstein systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2292–2303. https://doi.org/10.1109/TSMC.2022.3213809

    Article  Google Scholar 

  15. Hou, J., Chen, F., Li, P., & Zhu, Z. (2021). Gray-box parsimonious subspace identification of hammerstein-type systems. IEEE Transactions on Industrial Electronics, 68(10), 9941–9951. https://doi.org/10.1109/TIE.2020.3026286

    Article  Google Scholar 

  16. Manganiello, P., Ricco, M., Petrone, G., Monmasson, E., & Spagnuolo, G. (2015). Dual-kalman-filter-based identification and real-time optimization of pv systems. IEEE Transactions on Industrial Electronics, 62(11), 7266–7275. https://doi.org/10.1109/TIE.2015.2475240

    Article  Google Scholar 

  17. Ahmeid, M., Armstrong, M., Al-Greer, M., & Gadoue, S. (2018). Computationally efficient self-tuning controller for dc-dc switch mode power converters based on partial update kalman filter. IEEE Transactions on Power Electronics, 33(9), 8081–8090. https://doi.org/10.1109/TPEL.2017.2768618

    Article  Google Scholar 

  18. Padhee, S., Pati, U. C., & Mahapatra, K. (2018). Closed-loop parametric identification of dc-dc converter. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 232(10), 1429–1438. https://doi.org/10.1177/0959651818785291

    Article  Google Scholar 

  19. Correa, M. V., Aguirre, L. A., & Saldanha, R. R. (2002). Using steady-state prior knowledge to constrain parameter estimates in nonlinear system identification. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 49(9), 1376–1381. https://doi.org/10.1109/TCSI.2002.802345

    Article  Google Scholar 

  20. Aguirre, L. A., Donoso-Garcia, P. F., & Santos-Filho, R. (2000). Use of a priori information in the identification of global nonlinear models-a case study using a buck converter. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 47(7), 1081–1085. https://doi.org/10.1109/81.855463

    Article  Google Scholar 

  21. Hafiz, F., Swain, A., Mendes, E. M. A. M., & Aguirre, L. A. (2020). Multiobjective evolutionary approach to grey-box identification of buck converter. IEEE Transactions on Circuits and Systems I: Regular Papers, 67(6), 2016–2028. https://doi.org/10.1109/TCSI.2020.2970759

    Article  MathSciNet  Google Scholar 

  22. Qin, S. J. (2006). An overview of subspace identification. Computers & Chemical Engineering, 30(10–12), 1502–1513. https://doi.org/10.1016/j.compchemeng.2006.05.045

    Article  Google Scholar 

  23. Van der Veen, G., van Wingerden, J.-W., Bergamasco, M., Lovera, M., & Verhaegen, M. (2013). Closed-loop subspace identification methods: an overview. IET Control Theory & Applications, 7(10), 1339–1358. https://doi.org/10.1049/iet-cta.2012.0653

    Article  MathSciNet  Google Scholar 

  24. Katayama, T., et al. (2005). Subspace Methods for System Identification. Springer.

  25. Zhang, L., Zhou, D., Zhong, M., & Wang, Y. (2019). Improved closed-loop subspace identification based on principal component analysis and prior information. Journal of Process Control, 80, 235–246. https://doi.org/10.1016/j.jprocont.2019.06.001

    Article  Google Scholar 

  26. Hou, J., Chen, F., Li, P., & Zhu, Z. (2019). Prior-knowledge-based subspace identification for batch processes. Journal of Process Control, 82, 22–30. https://doi.org/10.1016/j.jprocont.2019.07.002

    Article  Google Scholar 

  27. Alenany, A., Shang, H., Soliman, M., & Ziedan, I. (2011). Improved subspace identification with prior information using constrained least squares. IET Control Theory & Applications, 5(13), 1568–15768. https://doi.org/10.1049/iet-cta.2010.0585

    Article  MathSciNet  Google Scholar 

  28. Trnka, P., & Havlena, V. (2009). Subspace like identification incorporating prior information. Automatica, 45(4), 1086–1091. https://doi.org/10.1016/j.automatica.2008.12.005

    Article  MathSciNet  Google Scholar 

  29. Sira-Ramirez, H., Perez-Moreno, R. A., Ortega, R., & Garcia-Esteban, M. (1997). Passivity-based controllers for the stabilization of dc-to-dc power converters. Automatica, 33(4), 499–513. https://doi.org/10.1016/S0005-1098(96)00207-5

    Article  MathSciNet  Google Scholar 

  30. Wang, J., Li, S., Yang, J., Wu, B., & Li, Q. (2015). Extended state observer-based sliding mode control for pwm-based dc–dc buck power converter systems with mismatched disturbances. IET Control Theory & Applications, 9(4), 579–586. https://doi.org/10.1049/iet-cta.2014.0220

    Article  MathSciNet  Google Scholar 

  31. Van Overschee, P., & De Moor, B. (2012). Subspace Identification for Linear Systems: Theory-Implementation-Applications. Springer.

  32. Kung, S.-Y. (1978). A new identification and model reduction algorithm via singular value decomposition. In 12th Asilomar Conference on Circuits, Systems and Computers, pp. 705–714. Pacific Grove, CA, USA.

  33. Hou, J., Chen, F., Li, P., & Zhu, Z. (2019). Fixed point iteration-based subspace identification of hammerstein state-space models. IET Control Theory & Applications, 13(8), 1173–1181. https://doi.org/10.1049/iet-cta.2018.6041

    Article  MathSciNet  Google Scholar 

  34. Hou, J. (2023). Parsimonious model based consistent subspace identification of hammerstein systems under periodic disturbances. International Journal of Control, Automation and Systems.https://doi.org/10.1007/s12555-022-0053-4

    Article  Google Scholar 

  35. Hou, J., Liu, T., & Chen, F. (2017). Orthogonal projection based subspace identification against colored noise. Control Theory and Technology, 15(1), 69–77. https://doi.org/10.1007/s11768-017-6003-7

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jie Hou, Zhen Yang, Huiming Wang, Jincheng Jiang & Xiaolei Chen

  2. School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China

    Taifu Li

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  1. Jie Hou
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Correspondence to Jie Hou.

Additional information

This work was supported in part by the Chongqing Natural Science Foundation (Nos. CSTB2022NSCQ-MSX1225, cstc2021jcyj-msxmX0142), in part by the Science and Technology Research Program of Chongqing Municipal Education Commission (Nos. KJQN202000602, KJQN202200626), in part by the National Natural Science Foundation of China (No. 61903057), in part by the China Postdoctoral Science Foundation (No. 2022MD713688) and in part by the Chongqing Postdoctoral Science Foundation (No. 2021XM3079).

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Hou, J., Yang, Z., Li, T. et al. Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters. Control Theory Technol. 22, 173–183 (2024). https://doi.org/10.1007/s11768-023-00148-9

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