An effective control system for the air supply in fuel cell systems (FCS) is required to prevent oxygen starvation and to maximize the net power. For this purpose, conventional feedback and adaptive controllers are designed using genetic programming (GP). To minimize the time required for the GP convergence, FCS models of different complexity are studied and a comparison between them is carried out. Guidelines on applying the GP approach based on data obtained from simulations are developed along with a specially designed cost function that promotes closed-loop linearization. The advantage of this design method lies in its applicability to complex nonlinear systems for which nonlinear control methods may not be applicable. Adaptation is added to the oxygen excess ratio (OER) regulation problem by training a neural network that provides the optimal OER reference based on the stack current and temperature. The performance of both the regulation and adaptive controllers is tested under noise in the compressor flow and the stack current measurements. The robustness of the GP controllers is observed through the frequency response analysis.

需要一种用于燃料电池系统（FCS）中的空气供应的有效控制系统，以防止缺氧并最大程度地增加净功率。为此，使用遗传编程（GP）设计常规的反馈和自适应控制器。为了最小化GP收敛所需的时间，研究了不同复杂度的FCS模型，并进行了比较。制定了基于从仿真中获得的数据应用GP方法的指南，以及专门设计的成本函数，该函数促进了闭环线性化。这种设计方法的优点在于它适用于非线性控制方法可能不适用的复杂非线性系统。通过训练神经网络，根据烟囱电流和温度提供最佳的OER参考，可将适应性添加到氧气过量比（OER）调节问题中。调节器和自适应控制器的性能均在压缩机流量和烟囱电流测量的噪声下进行测试。通过频率响应分析可以观察到GP控制器的鲁棒性。