Robust control of the PEM fuel cell hydrogen recirculation system using backstepping sliding mode control with RBF neural network AITranslate
Abstract AITranslate
The dynamic behavior of hydrogen gas in the anode recirculation loop of a proton exchange membrane fuel cell (PEMFC) is governed by coupled flow‑pressure interactions within the ejector, supply manifold, and stack channels, making precise pressure regulation a critical challenge for system efficiency, stability, and lifetime. Ensuring adequate anode fuel supply and stable hydrogen supply pressure requires meticulous control of the gas handling system. This study proposes a single‑ejector‑based backstepping sliding mode controller (BSMC) for PEMFC hydrogen recirculation systems. To compensate for the unknown gas flow disturbance induced by periodic purging, a radial basis function (RBF) neural network is integrated with the BSMC, which adaptively corrects the control law under different system states. Simulation results demonstrate that the proposed RBF‑compensated BSMC effectively mitigates purge‑induced pressure fluctuations. The controller achieves a maximum tracking error of 8.173 kPa, a mean absolute error of 0.17 kPa, and a root mean square error of 0.53 kPa. Consequently, the proposed control strategy offers superior stability and robustness compared to conventional proportional‑integral‑derivative (PID) controllers under step load current variations, variable pressure targets, and periodic purge disturbances.
KeyWords AITranslate
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Basic Information:
DOI：10.23919/CHAIN.2026.000021
Chinese Library Classification Number:
Citation Information:
The dynamic behavior of hydrogen gas in the anode recirculation loop of a proton exchange membrane fuel cell (PEMFC) is governed by coupled flow‑pressure interactions within the ejector, supply manifold, and stack channels, making precise pressure regulation a critical challenge for system efficiency, stability, and lifetime. Ensuring adequate anode fuel supply and stable hydrogen supply pressure requires meticulous control of the gas handling system. This study proposes a single‑ejector‑based backstepping sliding mode controller (BSMC) for PEMFC hydrogen recirculation systems. To compensate for the unknown gas flow disturbance induced by periodic purging, a radial basis function (RBF) neural network is integrated with the BSMC, which adaptively corrects the control law under different system states. Simulation results demonstrate that the proposed RBF‑compensated BSMC effectively mitigates purge‑induced pressure fluctuations. The controller achieves a maximum tracking error of 8.173 kPa, a mean absolute error of 0.17 kPa, and a root mean square error of 0.53 kPa. Consequently, the proposed control strategy offers superior stability and robustness compared to conventional proportional‑integral‑derivative (PID) controllers under step load current variations, variable pressure targets, and periodic purge disturbances.
quote
| GB/T 7714-2015 | [1] Caizhi Zhang, Yuanjie Gao, Yuan Feng, et al. Robust control of the PEM fuel cell hydrogen recirculation system using backstepping sliding mode control with RBF neural network[J]. Chain, 2026, 3(3): 434-452. DOI:10.23919/CHAIN.2026.000021. |
| MLA | [1] Caizhi Zhang, et al., "Robust control of the PEM fuel cell hydrogen recirculation system using backstepping sliding mode control with RBF neural network." Chain, vol. 3, no. 3, 2026, pp. 434-452, https://doi.org/10.23919/CHAIN.2026.000021. |
| APA | [1] Caizhi Zhang, Yuanjie Gao, Yuan Feng, Changhong Du, Tao Zeng, Junyu Yang, Yuansong Li, Dávid Kis, & Quan Liao. (2026). Robust control of the PEM fuel cell hydrogen recirculation system using backstepping sliding mode control with RBF neural network. Chain, 3(3), 434-452. https://doi.org/10.23919/CHAIN.2026.000021 |
| IEEE | [1] Caizhi Zhang, Yuanjie Gao, Yuan Feng, Changhong Du, Tao Zeng, Junyu Yang, Yuansong Li, Dávid Kis, and Quan Liao, "Robust control of the PEM fuel cell hydrogen recirculation system using backstepping sliding mode control with RBF neural network," Chain, vol. 3, no. 3, pp. 434-452, 2026, doi: 10.23919/CHAIN.2026.000021. keywords: {backstepping sliding-mode control;pressure control;proton exchange membrane fuel cell;radial basis function neural network} |