燃料电池阴极流量无模型自适应滑模控制器设计

doi:10.13229/j.cnki.jdxbgxb20220322

摘要/Abstract

摘要：

针对质子交换膜燃料电池阴极流量控制的问题，考虑到现有基于模型的控制方案对系统动力学的依赖性以及未建模动态对系统控制性能的影响，设计了一种新的基于数据驱动的无模型自适应控制方案。首先，采用非参数动态线性化技术得到阴极流量动态线性化数据模型，可实现对被控系统内部参数摄动和外部扰动的自适应性和鲁棒性。在该数据模型的基础上，设计了自适应二阶滑模控制器来改善系统的暂态品质和稳态精度。此外，在李雅普诺夫稳定性框架下证明了阴极流量系统在所提控制律驱动下进入到收敛的准滑动模态。最后，对于无模型自适应滑模算法中的参数，通过量子粒子群算法进行优化整定，多工况下验证了所提控制系统的有效性。

关键词: 控制科学与工程, 燃料电池, 无模型自适应控制, 二阶滑模控制, 量子粒子群优化

Abstract:

Aiming at the cathode flow control problem of proton exchange membrane fuel cells， considering the dependence of existing model-based control schemes on system dynamics and the influence of unmodeled dynamics on system control performance， a new data-driven model-free adaptive control scheme was designed. Firstly， the non-parametric dynamic linearization technology was used to obtain the dynamic linearized data model of cathode flow， which can realize the adaptability and robustness to the internal parameter perturbation and external perturbation of the controlled system. On the basis of this data model, an adaptive second-order sliding mode controller was designed to improve the transient quality and steady-state accuracy of the system. In addition， under the Lyapunov stability framework， it is proved that the cathode flow system finally enters a convergent quasi-sliding mode driven by the proposed control law. Finally， the parameters in the model-free adaptive sliding mode algorithm are determined by quantum particle swarm optimization. The effectiveness of the proposed control system is verified under multiple operating conditions.

Key words: control science and engineering, fuel cell, model-free adaptive control, second-order sliding mode control, quantum particle swarm optimization

中图分类号:

TP13

张冲,胡云峰,宫洵,孙耀. 燃料电池阴极流量无模型自适应滑模控制器设计[J]. 吉林大学学报(工学版), 2022, 52(9): 2085-2095.

Chong ZHANG,Yun-feng HU,Xun GONG,Yao SUN. Design of model⁃free adaptive sliding mode controller for cathode flow of fuel cell[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2085-2095.

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参考文献 25

1	王哲, 谢怡, 臧鹏飞, 等. 基于极小值原理的燃料电池客车能量管理策略[J]. 吉林大学学报: 工学版, 2020, 50(1): 36-43.
	Wang Zhe, Xie Yi, Zang Peng-fei, et al. Energy management strategy of fuel cell bus based on Pontryagin minimum principle[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(1): 36-43.
2	左曙光, 韩惠君, 苏虎, 等. 燃料电池车用旋涡风机气动噪声及影响因素[J]. 吉林大学学报: 工学版, 2013, 43(6): 1453-1458.
	Zuo Shu-guang, Han Hui-jun, Su Hu, et al. Analysis of a erodynamic noise and influence factors of a regenerative blower used in air supply system of fuel cell cars[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 43(6): 1453-1458.
3	Chi X, Lin F, Wang Y X. Disturbance and uncertainty immune onboard charging batteries with fuel cell by using equivalent load fuzzy logic estimation-based backstepping sliding mode control[J]. IEEE Transactions on Transportation Electrification, 2021, 7(3): 1249-1259.
4	Pukrushpan J T. Modeling and control of fuel cell systems and fuel processors[D]. Michigan, USA: University of Michigan, 2003.
5	Li Y H, Pei P C, Ma Z, et al. Analysis of air compression, progress of compressor and control for optimal energy efficiency in proton exchange membrane fuel cell[J]. Renewable and Sustainable Energy Reviews, 2020, 133: No. 110304.
6	Azad A, Fakhari I, Ahmadi P, et al. Analysis and optimization of a fuel cell integrated with series two-stage organic Rankine cycle with zeotropic mixtures[J]. International Journal of Hydrogen Energy, 2022, 47(5): 3449-3472.
7	Fan Z, Yu X L, Yan M, et al. Oxygen excess ratio control of pem fuel cell based on self-adaptive fuzzy PID[J]. IFAC-Papers OnLine, 2018, 51(31): 15-20.
8	Aliasghary M. Control of PEMFC systems using interval type-2 fuzzy PID approach[J]. Fuel Cells, 2018, 18(4): 449-456.
9	Arce A, Alejandro J, Bordons C, et al. Real-time implementation of a constrained MPC for efficient airflow control in a PEM fuel cell[J]. IEEE Transactions on Industrial Electronics, 2009, 57(6): 1892-1905.
10	Hu Y F, Chen H, Gong X, et al. Control-oriented modeling and robust nonlinear triple-step controller design for an air-feed system for polymer electrolyte membrane fuel cells[J]. Asian Journal of Control, 2019, 21(4): 1811-1823.
11	Ma Y, Zhang F, Gao J W, et al. Oxygen excess ratio control of PEM fuel cells using observer-based nonlinear triple-step controller[J]. International Journal of Hydrogen Energy, 2020, 45(54): 29705-29717.
12	Gruber J K, Bordons C, Oliva A. Nonlinear MPC for the airflow in a PEM fuel cell using a Volterra series model[J]. Control Engineering Practice, 2012, 20(2): 205-217.
13	Deng H W, Li Q, Cui Y L, et al. Nonlinear controller design based on cascade adaptive sliding mode control for PEM fuel cell air supply systems[J]. International Journal of Hydrogen Energy, 2019, 44(35): 19357-19369.
14	Hou Z, Jin S. Model Free Adaptive Control: Theory and Applications[M]. Boca Raton: CRC Press, 2013.
15	Yu W, Wang R, Bu X H, et al. Model free adaptive control for a class of nonlinear systems with fading measurements[J]. Journal of the Franklin Institute, 2020, 357(12): 7743-7760.
16	Liu S D, Hou Z S, Zhang X, et al. Model-free adaptive control method for a class of unknown MIMO systems with measurement noise and application to quadrotor aircraft[J]. IET Control Theory and Applications, 2020, 14(15): 2084-2096.
17	Jin M L, Lee J, Tsagarakis N G. Model-free robust adaptive control of humanoid robots with flexible joints[J]. IEEE Transactions on Industrial Electronics, 2016, 64(2): 1706-1715.
18	Tutsoy O, Barkana D E. Model free adaptive control of the under actuated robot manipulator with the chaotic dynamics[J]. ISA transactions, 2021, 118: 106-115.
19	Yuan D D, Wang Y K. Data driven model-free adaptive control method for quadrotor formation trajectory tracking based on rise and ismc algorithm[J]. Sensors, 2021, 21(4): 1289.
20	Mihoub M, Said N A, Abdennour B. Multimodel discrete second order sliding mode control: stability analysis and real time application on a chemical reactor[EB/OL]. [2022-03-01].
21	邢海燕, 刘超, 徐成, 等. 基于粒子群优化模糊C焊缝等级磁记忆定量识别模型[J]. 吉林大学学报:工学版, 2022, 52(3): 525-532.
	Xing Hai-yan, Liu Chao, Xu Cheng, et al. Quantitative metal magnetic memory classification model of weld grades based on particle swarm optimization fuzzy C means[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(3): 525-532.
22	韩飞, 郑明鹏. 基于三方竞争机制的反向多目标粒子群优化算法[J].江苏大学学报:自然科学版. 2021, 42(6): 685-693.
	Han Fei, Zheng Ming-peng. Opposition-based multi-objective particle swarm optimization algorithm based on tripartite competition mechanism[J]. Journal of Jiangsu University(Natural Science Edition), 2021, 42(6): 685-693.
23	庄星,韩飞.基于混合群智能算法优化BP神经网络的粮食产量预测[J].江苏大学学报:自然科学版, 2019, 40(2): 209-215.
	Zhuang Xing, Han Fei. Prediction of grain yield based on BP neural network optimized by hybrid swarm intelligence algorithm[J]. Journal of Jiangsu University(Natural Science Edition), 2019, 40(2): 209-215.
24	吴迪瑞,田韶鹏.前、后轴双电动机驱动纯电动客车的转矩分配策略[J].江苏大学学报:自然科学版, 2021, 42(6): 634-641.
	Wu Di-rui, Tian Shao-peng. Torque distribution strategy of pure electric bus with double motors driving by front and rear axles[J]. Journal of Jiangsu University(Natural Science Edition), 2021, 42(6): 634-641.
25	孙郁闻天. 基于量子行为的多目标粒子群优化算法的研究及其应用[D]. 镇江: 江苏大学计算机科学与通信工程学院, 2019.
	Sun Yu-wen-tian. A study of multi-objective particle swarm optimization based on quantum behavior and its application[D]. Zhenjiang: College of Computer Science and Communication Engineering, Jiangsu University, 2019.

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