Neural control of a boost converter: GRU modelling and reinforcement learning
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13709Keywords:
Reinforcement learning control, Nonlinear system identification, Machine Learning, Learning for control, Data-based control, Identification for control, Model ValidationAbstract
This paper presents a neural control strategy applied to a DC/DC boost converter, combining plant identification using a recurrent GRU network with the design of a reinforcement learning controller. The control problem is formulated considering
an inner current loop and an outer voltage loop. A GRU network is trained to reproduce the converter dynamics from the duty cycle, inductor current, output voltage and load current, predicting increments rather than absolute values to mitigate error
accumulation. This neural plant is used as the training environment for a TD3 agent, whose action is the duty cycle. The resulting controller is integrated into a cascaded structure with an external PI voltage loop and implemented on an experimental platform
based on Imperix modules, validating its tracking capability and disturbance rejection on a real converter.
References
Bonassi, F., Farina, M., Scattolini, R., 2021. On the stability properties of gated recurrent units neural networks. Systems & Control Letters 157, 105049. DOI: 10.1016/j.sysconle.2021.105049
Cheng, H., Jung, S., Kim, Y.-B., 2025. A novel reinforcement learning controller for the dc-dc boost converter. Energy 321, 135479. DOI: 10.1016/j.energy.2025.135479
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pp. 1724–1734. DOI: 10.3115/v1/D14-1179
Fujimoto, S., Hoof, H. v., Meger, D., 2018. Addressing function approximation error in actor-critic methods. In: Proceedings of the 35th International Conference on Machine Learning. pp. 1587–1596.
Guldemir, H., 2005. Sliding mode control of dc-dc boost converter. Journal of Applied Sciences 5 (3), 588–592.
Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Computation 9 (8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735
Ławry´nczuk, M., Zarzycki, K., 2025. Lstm and gru type recurrent neural networks in model predictive control: A review. Neurocomputing 632, 129712. DOI: 10.1016/j.neucom.2025.129712
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D., 2016. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
Niu, R., Zhang, H., Song, J., 2023. Model predictive control of dc–dc boost converter based on generalized proportional integral observer. Energies 16 (3), 1245. DOI: 10.3390/en16031245
Rajamallaiah, A., Karri, S. P. K., Shankar, Y. R., 2024. Deep reinforcement learning based control strategy for voltage regulation of DC-DC buck converter feeding CPLs in DC microgrid. IEEE Access 12, 17419–17430. DOI: 10.1109/ACCESS.2024.3358412
Saha, U., Jawad, A., Shahria, S., Rashid, A. H.-U., 2024. Proximal policy optimization-based reinforcement learning approach for dc-dc boost converter control: A comparative evaluation against traditional control techniques. Heliyon 10 (18), e37823. DOI: 10.1016/j.heliyon.2024.e37823
Sutton, R. S., Barto, A. G., 2018. Reinforcement learning: An introduction. Verma, P., Anwar, M. N., Ram, M. K., Iqbal, A., 2023. Internal model control scheme-based voltage and current mode control of dc–dc boost converter. IEEE Access 11, 110558–110569. DOI: 10.1109/ACCESS.2023.3320272
Wang, Y., et al., 2024. TD3 algorithm based reinforcement learning control for multiple-input multiple-output DC–DC converters. IEEE Journal of Emerging and Selected Topics in Power Electronics. DOI: 10.1109/JESTPE.2024.3564013
Za’ter, M. E., 2022. Modelling of a DC-DC buck converter using long-shortterm-memory (LSTM). arXiv preprint arXiv:2211.03040.
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Copyright (c) 2026 Juan Enseñat, Pablo Serrano-Torres, Gerson Portilla, Carolina Albea Sánchez, Alexandre Seuret
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