Skip to main content
Log in

Self-learning Controller Design for DC–DC Power Converters with Enhanced Dynamic Performance

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

This article presents a promising self-learning-based robust control for output voltage tracking in DC–DC buck power converters, particularly for applications demanding high precision performance in face of large load uncertainties. The design involves a computationally simple online single hidden layer neural network, to rapidly estimate the unanticipated load changes and exogenous disturbances over a wide range. The controller is designed within a backstepping framework and utilizes the learnt uncertainty from the neural network for subsequent compensation, to eventually ensure an asymptotic stability of the tracking error dynamics. The results obtained feature a significant improvement of dynamic and steady-state performance concurrently for both output voltage and inductor current in contrast to other competent control strategies lately proposed in the literature for similar applications. Extensive numerical simulations and experimentation on a developed laboratory prototype are carried out to justify the practical applicability and feasibility of the proposed controller. Experimental results substantiate the claims of fast dynamic performance in terms of 94% reduction in the settling time, besides an accurate steady-state tracking for both output voltage and inductor current. Moreover, the close resemblance between computational and experimental results is noteworthy and unveils the immense potential of the proposed control system for technology transfer.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  • Behera, L., & Kar, I. (2010). Intelligent systems and control principles and applications. Oxford University Press Inc.

  • Bouchama, Z., Khatir, A., Benaggoune, S., & Harmas, M. N. (2020). Design and experimental validation of an intelligent controller for DC–DC buck converters. Journal of the Franklin Institute, 357(15), 10353–10366.

    Article  MathSciNet  Google Scholar 

  • Boutebba, O., Semcheddine, S., Krim, F., & Talbi, B. (2019). Adaptive nonlinear controller design for DC–DC buck converter via backstepping methodology. In 2019 international conference on advanced electrical engineering (ICAEE), (pp. 1–7). https://doi.org/10.1109/ICAEE47123.2019.9014825

  • Chen, J., Chen, Y., Tong, L., Peng, L., & Kang, Y. (2020). A backpropagation neural network-based explicit model predictive control for DC–DC converters with high switching frequency. IEEE Journal of Emerging and Selected Topics in Power Electronics, 8(3), 2124–2142.

    Article  Google Scholar 

  • Cheng, C.-H., Cheng, P.-J., & Wu, M.-T. (2010). Fuzzy logic design of self-tuning switching power supply. Expert Systems with Applications, 37(4), 2929–2936.

    Article  Google Scholar 

  • Deylamani, M. J., Amiri, P., & Refan, M. H. (2019). Design and stability analysis of a discrete-time sliding mode control for a synchronous DC–DC buck converter. International Journal of Control, Automation and Systems, 17, 1393–1407.

    Article  Google Scholar 

  • Dong, W., Li, S., Fu, X., Li, Z., Fairbank, M., & Gao, Y. (2021). Control of a buck DC/DC converter using approximate dynamic programming and artificial neural networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 68(4), 1760–1768.

    Article  Google Scholar 

  • El Fadil, H., Giri, F., Haloua, M., & Ouadi, H. (2003). Nonlinear and adaptive control of buck power converters. In 42nd IEEE international conference on decision and control (IEEE Cat. No.03CH37475) (vol. 5, pp. 4475–4480). https://doi.org/10.1109/CDC.2003.1272244

  • Gangula, S. D., Nizami, T. K., Ramanjaneya Reddy, U., & Singh, P. (2023). Real-time implementation of Laguerre neural network-based adaptive control of DC–DC converter. In Soft computing: Theories and applications: Proceedings of SoCTA 2022 (pp. 721–731). Springer.

  • Hekimoğlu, B., & Ekinci, S. (2020). Optimally designed PID controller for a DC–DC buck converter via a hybrid whale optimization algorithm with simulated annealing. Electrica, 20(1), 19–27.

    Article  Google Scholar 

  • Ioinovici, A. (2013). Power electronics and energy conversion systems: Fundamentals and hard-switching converters (Vol. 1). Wiley Online Library.

  • Izci, D., Hekimoğlu, B., & Ekinci, S. (2022). A new artificial ecosystem-based optimization integrated with Nelder–Mead method for PID controller design of buck converter. Alexandria Engineering Journal, 61(3), 2030–2044.

    Article  Google Scholar 

  • Kavitha, A., & Uma, G. (2010). Control of chaos in SEPIC DC–DC converter. International Journal of Control, Automation and Systems, 8, 1320–1329.

    Article  Google Scholar 

  • Khalil, H. K. (2002). Nonlinear systems (3rd ed.). Patience-Hall.

  • Kim, S.-K., Kim, K.-C., & Ki Ahn, C. (2021). Output-voltage-tracking control for buck converters using variable convergence rate mechanism without current feedback. IEEE Transactions on Industrial Electronics, 69(3), 2938–2946.

    Article  Google Scholar 

  • Komurcugil, H. (2012). Adaptive terminal sliding-mode control strategy for DC–DC buck converters. ISA Transactions, 51(6), 673–681.

    Article  Google Scholar 

  • Krstic, M., Kokotovic, P. V., & Kanellakopoulos, I. (1995). Nonlinear and adaptive control design. John Wiley & Sons Inc.

  • Ma, H., Wang, Z., Wang, D., Liu, D., Yan, P., & Wei, Q. (2015). Neural-network-based distributed adaptive robust control for a class of nonlinear multiagent systems with time delays and external noises. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(6), 750–758.

    Article  Google Scholar 

  • Maccari, L. A., Montagner, V. F., & Ferreira, A. A. (2013). A linear quadratic control applied to buck converters with H-infinity constraints. In 2013 Brazilian power electronics conference (pp. 339–344). IEEE.

  • Madonski, R., Łakomy, K., & Yang, J. (2021). Simplifying ADRC design with error-based framework: Case study of a DC–DC buck power converter. Control Theory and Technology, 19, 94–112.

    Article  MathSciNet  Google Scholar 

  • Mall, S., & Chakraverty, S. (2016). Application of Legendre neural network for solving ordinary differential equations. Applied Soft Computing, 43, 347–356.

    Article  Google Scholar 

  • Nizami, T. K., & Chakravarty, A. (2020). Laguerre neural network driven adaptive control of DC–DC step down converter. IFAC-PapersOnLine, 53(2), 13396–13401.

    Article  Google Scholar 

  • Nizami, T. K., Chakravarty, A., & Mahanta, C. (2021). Time bound online uncertainty estimation based adaptive control design for DC–DC buck converters with experimental validation. IFAC Journal of Systems and Control, 15, 100127.

    Article  MathSciNet  Google Scholar 

  • Piao, C., Jiang, C., Qiao, H., Cho, C., & Lu, S. (2014). Modeling and implementation of fixed switching frequency sliding mode control to two-stage DC–DC converter. International Journal of Control, Automation and Systems, 12, 1225–1233.

    Article  Google Scholar 

  • Rajamani, M. P. E., Rajesh, R., & Willjuice Iruthayarajan, M. (2023). Design and experimental validation of PID controller for buck converter: A multi-objective evolutionary algorithms based approach. IETE Journal of Research, 69(1), 21–32.

    Article  Google Scholar 

  • Ramirez-Hernandez, J., Hernandez-Gonzalez, L., Juarez-Sandoval, O. U., Garcia-Fernandez, J. P., & Bote-Vazquez, M. Y. (2021). Artificial neural network based on a predictive current control in a DC–DC buck converter. In 2021 IEEE international autumn meeting on power, electronics and computing (ROPEC) (vol. 5, pp. 1–6). IEEE.

  • Ramırez-Hernandez, J., Juárez-Sandoval, O. U., Cano-Pulido, K., Márquez-Rubio, J. F., & Mondragon-Escamilla, N. (2019). Online learning artificial neural network controller for a buck converter. In 2019 IEEE international autumn meeting on power, electronics and computing (ROPEC) (pp. 1–5). IEEE

  • Rashid, M. H. (2017). Power electronics handbook. Butterworthheinemann.

  • Renjini, G., & Devi, V. (2022). Artificial neural network controller based cleaner battery-less fuel cell vehicle with EF2 resonant DC–DC converter. Sustainable Computing: Informatics and Systems, 35, 100667.

    Google Scholar 

  • Sahoo, D. M., & Chakraverty, S. (2017). Functional link neural network learning for response prediction of tall shear buildings with respect to earthquake data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(1), 1–10.

    Google Scholar 

  • Shieh, C.-S. (2014). Fuzzy PWM based on genetic algorithm for battery charging. Applied Soft Computing, 21, 607–616.

    Article  Google Scholar 

  • Silva-Ortigoza, R., Hernández-Guzmán, V. M., Antonio-Cruz, M., & Munoz-Carrillo, D. (2014). DC/DC buck power converter as a smooth starter for a DC motor based on a hierarchical control. IEEE Transactions on Power Electronics, 30(2), 1076–1084.

    Article  Google Scholar 

  • Sira-Ramirez, H., Rios-Bolivar, M., & Zinober, A. S. (1995). Adaptive input-output linearization for PWM regulation of DC-to-DC power converters. In Proceedings of 1995 American control conference–ACC’95 (vol. 1, pp. 81–85). IEEE.

  • Soriano-Sánchez, A. G., Rodríguez-Licea, M. A., Pérez-Pinal, F. J., & Vázquez-López, J. A. (2020). Fractional-order approximation and synthesis of a PID controller for a buck converter. Energies, 13(3), 629.

    Article  Google Scholar 

  • Sureshkumar, R., & Ganeshkumar, S. (2011). Comparative study of proportional integral and backstepping controller for buck converter. In 2011 international conference on emerging trends in electrical and computer technology (pp. 375–379). IEEE.

  • Taheri, B., Sedaghat, M., Bagherpour, M. A., & Farhadi, P. (2019). A new controller for DC–DC converters based on sliding mode control techniques. Journal of Control, Automation and Electrical Systems, 30, 63–74.

    Article  Google Scholar 

  • Tan, S.-C., Lai, Y.-M., & Tse Chi, K. (2008). General design issues of sliding-mode controllers in DC–DC converters. IEEE Transactions on Industrial Electronics, 55(3), 1160–1174.

    Article  Google Scholar 

  • Utkin, V. (2013). Sliding mode control of DC/DC converters. Journal of the Franklin Institute, 350(8), 2146–2165.

  • 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.

    Article  MathSciNet  Google Scholar 

  • Wang, J., Rong, J., & Li, Yu. (2022). Dynamic prescribed performance sliding mode control for DC–DC buck converter system with mismatched time-varying disturbances. ISA Transactions, 129, 546–557.

  • Wang, L., Liu, Y., Gu, K., & Wu, T. (2020). A radial basis function artificial neural network (RBF ANN) based method for uncertain distributed force reconstruction considering signal noises and material dispersion. Computer Methods in Applied Mechanics and Engineering, 364, 112954.

    Article  MathSciNet  Google Scholar 

  • Wang, L., Yang, G., Li, Z., & Xu, F. (2021). An efficient nonlinear interval uncertain optimization method using Legendre polynomial chaos expansion. Applied Soft Computing, 108, 107454.

    Article  Google Scholar 

  • Yang, B., Li, W., Zhao, Y., & He, X. (2009). Design and analysis of a grid-connected photovoltaic power system. IEEE Transactions on Power Electronics, 25(4), 992–1000.

    Article  Google Scholar 

  • Zhang, X., Zhang, Z., Bao, H., Bao, B., & Qu, X. (2020). Stability effect of control weight on multiloop COT-controlled buck converter with PI compensator and small output capacitor ESR. IEEE Journal of Emerging and Selected Topics in Power Electronics, 9(4), 4658–4667.

    Article  Google Scholar 

  • Zhou, L., Yi, X., Jiang, Z., She, J., & Zhang, Z. (2022). Generalized-extended-state-observer-based sliding-mode control for buck converter systems. International Journal of Control, Automation and Systems, 20(12), 3923–3931.

    Article  Google Scholar 

Download references

Acknowledgements

The statements made herein are solely the responsibility of the authors.

Funding

This publication is made possible by the TARE grant [TAR/ 2020/000386] from the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tousif Khan Nizami.

Ethics declarations

Conflict of interest

The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

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

Gangula, S.D., Nizami, T.K., Udumula, R.R. et al. Self-learning Controller Design for DC–DC Power Converters with Enhanced Dynamic Performance. J Control Autom Electr Syst 35, 532–547 (2024). https://doi.org/10.1007/s40313-024-01086-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-024-01086-w

Keywords

Navigation