Real-time implementation of a neural model-based self-tuning PID strategy for oxygen stoichiometry control in PEM fuel cell

This paper proposes a real-time implementable self-tuning PID control strategy to tackle oxygen excess ratio regulation challenge of a proton exchange membrane fuel cell. Controller parameters are updated on-line, at each sampling time, using a not iterative procedure based on an artificial neural network model. The proposed controller takes account of nonlinear behaviors of the process, while avoiding heavy computations.