质子交换膜燃料电池（Proton Exchange Membrane Fuel Cell，PEMFC）由于具有能量密度高、能量转化率高、清洁环保等特点而受到世界各国的关注，目前已经在新能源汽车、高技术船舶上得到初步应用。PEMFC供气系统是一个具有非线性和参数不确定性的多输入多输出系统，过氧比（Oxygen Excess Ratio，OER）对电池的寿命和输出功率影响较大，为了在负荷波动和供气波动造成的不同干扰情况下准确控制过氧比，确保电池健康稳定工作和最大功率点运行，论文利用进化算法、模糊理论对传统PID控制器进行改进，设计了一种新的PEMFC供气系统控制器，论文主要研究内容如下：

    首先，论文提出了一种基于电磁场优化算法（Electromagnetic Field Optimization， EFO）的PEMFC供气系统模糊控制器参数优化方法，该控制器由反馈回路中的模糊PID控制器和前馈回路中的模糊控制器组成。利用EFO算法对两种控制器的参数进行同时优化，优化后的模糊控制器具有优良的跟踪和抗干扰性能，在PEMFC运行于不同模式时的测试结果表明论文提出的混合模糊控制器性能优于已有的其他控制器性能。

    其次，提出了一种神经网络算法（Neural Network Algorithm，NNA）与分数阶模糊PID控制器（Fractional-Order Fuzzy Proportional Integral Derivative，FOFPID）相结合的PEMFC控制系统，采用神经网络对FOFPID控制器的参数进行在线优化，根据系统实时变化对FOFPID控制器参数进行动态调整。在神经网络在线优化的基础上，论文进一步设计了一种神经网络与差分进化（Differential Evolution， DE）相结合的FOPID控制器参数优化方法，提高了单纯利用神经网络进行参数优化时的算法收敛速度，将提出的混合NNA-DE算法应用于基于观测器的区间二型模糊PID控制器的优化，测试结果表明所提出的NNA-DE-FOPID控制器在PEMFC运行于不同模式时具有良好的跟踪和干 扰抑制性能。

    第三，论文提出了一种改进的反向神经网络算法（Opposition Based Neural Network Algorithm，OB-NNA）和准反向神经网络算法（Quasi Opposition Based Neural Network Algorithm，QOB-NNA），将提出的OB-NNA、QOB-NNA算法应用于基于观测器的分数阶区间二型模糊PID控制器的优化设计，测试了所提出的方法与其他算法的性能指标，结果表明所提出的OB-NNA算法和QOB-NNA算法在精度、收敛速度等指标上优于其他算法。

综上所述，论文所提出的基于进化算法、模糊原理及PID控制器相结合供气控制系统具有良好的跟踪和抗干扰性能，在质子交换膜燃料电池运行在不同模式时能够获得稳定的过氧比，具有实际应用推广的可行性。

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