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Technical Brief

Robust Adaptive Control of PEMFC Air Supply System Based on Radical Basis Function Neural Network

[+] Author and Article Information
Yun-Long Wang

School of Mechanical Engineering and Automation,
Northeastern University,
Shenyang, Liaoning 110819, China
e-mail: wyl_neu@163.com

Yong-Fu Wang

School of Mechanical Engineering and Automation,
Northeastern University,
Shenyang, Liaoning 110819, China
e-mail: yfwang@mail.neu.edu.cn

Hua-Kai Zhang

School of Mechanical Engineering and Automation,
Northeastern University,
Shenyang, Liaoning 110819, China
e-mail: zhk@sia.cn

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received September 13, 2018; final manuscript received January 21, 2019; published online February 27, 2019. Assoc. Editor: Huiping Li.

J. Dyn. Sys., Meas., Control 141(6), 064503 (Feb 27, 2019) (7 pages) Paper No: DS-18-1427; doi: 10.1115/1.4042674 History: Received September 13, 2018; Revised January 21, 2019

This technical brief emphasizes on the control of polymer electrolyte membrane fuel cell (PEMFC) air supply system. The control objective is to improve the net power output through adjusting the oxygen excess ratio within a reasonable range. In view of the problem that the PEMFC air supply system is difficult to achieve accurate modeling and stable control, a robust adaptive controller is proposed by utilizing exact linearization and radical basis function (RBF) neural network (RBFNN) system. This controller does not need the complete structure and parameters of PEMFC system. The unmodeled dynamics of PEMFC system can be approximated by RBFNN in which the adaptive learning law can be derived based on Lyapunov theory, and the external disturbance as well as the approximation error of RBFNN can be attenuated through robust control. The stability analysis shows that the system tracking error is uniformly ultimately bounded. Finally, the effectiveness and feasibility of controller are validated by hardware-in-loop (HIL) experiment.

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Figures

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Fig. 1

Steady-state analysis of the system performance in different load conditions

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Fig. 2

Structure diagram of PEMFC system

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Fig. 3

The structure block diagram of adaptive robust RBFNN controller

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Fig. 4

Hardware-in-loop test bench

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Fig. 7

Compressor supply voltage

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Fig. 8

Tracking of oxygen excess ratio

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Fig. 9

Tracking error of oxygen excess ratio

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Fig. 10

Compressor flow rate

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Fig. 11

Comparison between adaptive robust RBFNN controller and PID controller at different stack temperatures

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