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Integrated flight/propulsion optimal control for DPC aircraft based on the GA-RPS algorithm

机译:基于GA-RPS算法的DPC飞机综合飞行/推进最优控制

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摘要

The new distributed propulsion configuration (DPC) can effectively improve fuel efficiency and reduce pollution, but also brings in special cross-coupling effects between the flight and propulsion systems. To tackle this problem, integrated flight/propulsion modeling and optimal control of DPC with boundary layer ingestion (BLI) and supercirculation features are systematically investigated. As the basis, by taking the inherent BLI and supercirculation features into consideration, an integrated flight/propulsion model of SAX-40 is built to reflect the actual complex engine-aircraft coupling effects. Then an integrated flight/propulsion optimal control scheme is proposed to deal with the strong coupling effects and to implement the comprehensive control of redundant control surfaces as well as the distributed engines. Under this scheme, a detailed description of the optimal control problem is presented, and a novel two-stage optimization algorithm named genetic algorithm-random pattern search (GA-RPS) is proposed to solve this complex optimal problem. By the new strategies of parallel genetic algorithm computing in sub-regions and random pattern search in the GA-RPS algorithm, the optimization accuracy and convergence speed are effectively improved. Simulation results demonstrate the effectiveness of the GA-RPS algorithm and the significant performance improvement to DPC by optimization.
机译:新的分布式推进配置(DPC)可以有效地提高燃油效率并减少污染,而且还可以在飞行系统和推进系统之间带来特殊的交叉耦合效果。为了解决这个问题,系统地研究了具有边界层摄入(BLI)和超环流特征的DPC的综合飞行/推进建模和最优控制。作为基础,通过考虑固有的BLI和超循环特性,建立了SAX-40的综合飞行/推进模型,以反映实际的复杂发动机-飞机耦合效果。然后,提出了一种综合的飞行/推进最优控制方案,以应对强耦合效应,并实现对冗余控制面和分布式发动机的综合控制。在该方案下,给出了最优控制问题的详细描述,并提出了一种新颖的两阶段优化算法-遗传算法-随机模式搜索(GA-RPS)来解决该复杂的最优问题。通过子区域并行遗传算法计算和GA-RPS算法中随机模式搜索的新策略,有效提高了优化精度和收敛速度。仿真结果证明了GA-RPS算法的有效性以及通过优化对DPC的显着性能改进。

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