A neural networks based predictive voltage-tracking controller design for proton exchange membrane fuel cell model

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive volta...

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Bibliographic Details
Published in:	Journal of Engineering Vol. 25; no. 12; pp. 26 - 48
Main Authors:	al-Araji, Ahmad Sabah Abd al-Amir, Dahad, Haydar Abd, Jabr, Isra Abbas
Format:	Journal Article
Language:	English
Published:	Baghdad, Iraq University of Baghdad, College of Engineering 21-11-2019 University of Baghdad
Subjects:	Neural Networks, Predictive Controller, Fuel Cell, Particle Swarm Optimization البروتونات الشبكات العصبية خلايا الوقود
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Summary:	In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.
ISSN:	1726-4073 2520-3339
DOI:	10.31026/j.eng.2019.12.03