Adaptive neural control of PEMFC system based on data-driven and reinforcement learning approaches - Université de La Réunion Access content directly
Journal Articles Control Engineering Practice Year : 2022

Adaptive neural control of PEMFC system based on data-driven and reinforcement learning approaches

Christophe Lin-Kwong-Chon
Cédric Damour
Michel Benne
Jean-Jacques Amangoua Kadjo
Brigitte Grondin-Pérez
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Abstract

Proton exchange membrane fuel cell systems are being increasingly put forward as hydrogen energy carrier converters. Recent advancements in reliability strategies have been stimulated through maintaining a healthy operating condition of the system and covering plant faults. However, it is observed that occurrence or even the mitigation of these faults cause multilateral effects that can potentially destabilize the normal operation of the system. In the active fault tolerant control strategy, two modules are designed to fault management. The diagnostic module identifies the apparent fault and identifies the corrective commands, then the re-design module adapts the controller to dynamic system changes. In order to improve the generic characteristics of the re-design module, this paper presents a data-driven neural controller capable to automatically adapt to system health states. The developed approach comes from the machine learning class and combines adaptive dynamic programming, deep echo-state neural network models and fuzzy logic learning. The proposed controller is evaluated under occurrence of channels flooding and membrane drying faults, but also actuators and water purging disturbances. Simulation and experimental results show the effectiveness of the proposed data-driven approach without prior neural model training, while guaranteeing the stability and learning convergence of the adaptive controller.
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hal-04110907 , version 1 (20-06-2023)

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Christophe Lin-Kwong-Chon, Cédric Damour, Michel Benne, Jean-Jacques Amangoua Kadjo, Brigitte Grondin-Pérez. Adaptive neural control of PEMFC system based on data-driven and reinforcement learning approaches. Control Engineering Practice, 2022, 120, pp.105022. ⟨10.1016/j.conengprac.2021.105022⟩. ⟨hal-04110907⟩
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