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Robust HȡE; suboptimal control based on effective informed adaptive particle swarm optimisation algorithm for proton exchange membrane fuel cell system

机译:基于有效知情自适应粒子群算法的质子交换膜燃料电池系统鲁棒H ȡE; 次优控制

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摘要

A novel robust HȡE; control strategy used to adjust the oxygen excess ratio on the cathode side for proton exchange membrane fuel cell (PEMFC) system is proposed. This robust H1 suboptimal control method is based on an effective informed adaptive particle swarm optimisation algorithm with better equilibrium characteristic between global search and local search. Owing to the existence of uncertainties and disturbances, the setup of objectives associated with various inequality constraints and the selection of weighting functions is considered as an optimisation problem in the framework of HȡE; mixed sensitivity and structure singular value analysis. The simulation results on the running process of an electrical vehicle demonstrate that this proposed method can maintain oxygen excess ratio better at the optimal operating point, so that the net output power from the PEMFC system could be increased with the decrease of the parasitic power. In comparison with prior control approaches from other researchers, the method addressed in this study has exhibited better robust performance in the presence of multiple uncertain parameters and disturbances. The feasibility and the validity of the robust HȡE; suboptimal control method proposed have been proven qualitatively from the experimental study.
机译:提出了一种新颖的鲁棒H E 控制策略,该策略用于调节质子交换膜燃料电池(PEMFC)系统阴极侧的氧过量比。这种鲁棒的H1次优控制方法基于有效的知情自适应粒子群优化算法,在全局搜索和局部搜索之间具有更好的平衡特性。由于存在不确定性和扰动,在H ȡE框架下,与各种不等式约束相关的目标的建立和加权函数的选择被认为是一个优化问题; 混合灵敏度和结构奇异值分析。在电动汽车运行过程中的仿真结果表明,该方法可以在最佳工作点更好地保持氧气过剩率,从而可以随着寄生功率的降低而增加PEMFC系统的净输出功率。与其他研究人员先前的控制方法相比,本研究中提出的方法在存在多个不确定参数和干扰的情况下表现出更好的鲁棒性能。实验证明了所提出的鲁棒H ȡE; 次优控制方法的可行性和有效性。

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