控制理论与应用2025，Vol.42Issue(8)：1587-1595,9.DOI:10.7641/CTA.2025.40416
基于模型自学习的燃料电池空气系统自抗扰控制
Active disturbance rejection control of fuel cell air system based on model self-learning
摘要
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.关键词
质子交换膜燃料电池/模型训练/阶次选择/自抗扰控制Key words
proton exchange membrane fuel cells(PEMFC)/model training/order selection/active disturbance rejec-tion control引用本文复制引用
江维海,程放,李丞,朱仲文,季传龙..基于模型自学习的燃料电池空气系统自抗扰控制[J].控制理论与应用,2025,42(8):1587-1595,9.基金项目
安徽省科技重大专项项目(202203a05020006),先进内燃动力全国重点实验室开放课题重点项目(K2023-2),中央高校基本科研业务费专项资金项目(JZ2024HGTB0234)资助.Supported by the Anhui Provincial Major Science and Technology Project(202203a05020006),the Open Key Project of National Key Laboratory of Advanced Internal Combustion Power(K2023-2)and the Fundamental Research Funds for the Central Universities(JZ2024HGTB0234). （202203a05020006）
