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Robust composite adaptive neural network control for air management system of PEM fuel cell based on high-gain observer

  • Yunlong Wang
  • Yongfu WangEmail author
  • Gang Chen
Original Article
  • 60 Downloads

Abstract

Polymer electrolyte membrane (PEM) fuel cell system is usually affected negatively by external disturbance, model uncertainties and unmeasured variables. In this paper, a robust composite adaptive neural network controller using high-gain observer is proposed to achieve stable oxygen excess ratio control for PEM fuel cell air management system. First, the derivatives of system output, which are unavailable due to the limited sensors, are estimated via high-gain observer. Then, a neural network is adopted to estimate the unknown system dynamics and the additional robust term is used to attenuate the compound disturbance including unknown external disturbance and neural network approximation error. Finally, a composite adaptive updating laws are constructed by utilizing estimated tracking error and modeling error to improve the tracking performance. In contrast to the existing controllers applied in PEM fuel cell air management system, this controller has a better control performance in the practical application. By means of Lyapunov stability analysis, it is theoretically proved that the system tracking error is uniformly ultimately bounded. The effectiveness and practicability of the proposed controller are validated by hardware-in-loop experiment.

Keywords

Composite adaptive control Neural network High-gain observer Polymer electrolyte membrane (PEM) fuel cell 

Abbreviations

PEM

Polymer electrolyte membrane

OER

Oxygen excess ratio

HIL

Hardware-in-loop

RBFNN

Radial basis function neural network

CARBFNN

Composite adaptive radial basis function neural network

PID

Proportion–integral–derivative

RMSE

Root mean square error

SD

Standard deviation

Notes

Funding

Natural Science Foundation of China (Grand No. 51775103).

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical Engineering and AutomationNortheastern UniversityShenyangChina
  2. 2.College of IT and EngineeringMarshall UniversityHuntingtonUSA

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