基于ALMBO算法的PEMFC分数阶时域子空间模型研究

针对质子交换膜燃料电池（PEMFC）发电过程复杂难以建模的问题，考虑PEMFC系统的分数阶特性，提出一种基于优化的分数阶时域子空间辨识方法，并建立PEMFC的分数阶状态空间模型。首先，将分数阶微分理论与子空间时域辨识方法相结合，采用Poisson滤波器对输入输出信号进行滤波处理，并引入权重矩阵提高辨识的精度；其次，对Poisson滤波器以及辨识的分数阶阶次寻优，提出一种变异反向学习的自适应帝王蝶优化算法（ALMBO），在迁移算子中引入变异反向学习策略、并融入自适应权重来提高寻优的精度，防止陷入局部最优解。最后，通过仿真结果验证算法的有效性，所得的PEMFC辨识模型能准确描述PEMFC的动态过程。

RESEARCH ON FRACTIONAL ORDER TIME DOMAIN SUBSPACE MODEL OF PEMFC BASED ON ALMBO ALGORITHM

The power generation process of proton exchange membrane fuel cell （PEMFC） is complex to describe, this paper, considering the fractional order characteristics of PEMFC system, proposes an optimization based fractional order time domain subspace identification method, and establishes a fractional order state space model of PEMFC. The fractional differential theory is combined with subspace identification method, and Poisson filter is used to filter the input and output data. The weight matrix is introduced to improve the accuracy of identification. Then, a mutation reverse learning adaptive monarch butterfly optimization algorithm （ALMBO） is proposed for the identification of fractional order and other parameters. The mutation reverse learning strategy is introduced into the transfer operator, and the adaptive weight is integrated to improve the optimization accuracy and prevent falling into the local optimal solution. Finally, the simulation results verify the effectiveness of the algorithm, and the identification model can accurately describe the dynamic process of PEMFC.