Active disturbance rejection control of fuel cell air system based on model self-learning
JIANG Wei-hai
CHENG Fang
LI Cheng
ZHU Zhong-wen
JI Chuan-long
Abstract：The control-oriented model of the fuel cell air system exhibits limited adaptability to operating conditions,thereby exacerbating the inaccuracies caused by temporal changes in the system and subsequently compromising its control effectiveness.To overcome this,a study presents an active disturbance rejection control approach rooted in Bayesian learn-ing,aimed at precise control across broad operating conditions and longevity.A fourth-order model tailored for fuel cell air systems is formulated,with Bayesian estimation refining parameters using test data from diverse working conditions.Maximum likelihood estimation selects the optimal polynomial order for the air compressor flow model,mitigating the impact of time-varying parameters and enhancing model adaptability.Addressing flow-pressure coupling,an active dis-turbance rejection decoupling control is introduced.Treating coupling as systemic disturbance,an extended state observer estimates and compensates in real-time,enabling precise decoupling.A MATLAB/Simulink simulation platform validates the method.Results highlight its effectiveness in ensuring model accuracy,facilitating precise flow-pressure control,and ensuring safe,efficient fuel cell system operation.
Keywords：proton exchange membrane fuel cells(PEMFC)model trainingorder selectionactive disturbance rejec-tion control
Publication Date：2025-08-30
Online Publishing Date：2025-10-10(First online date of this platform, not the publication date of the document)
Pages：9( 1587-1595 )