On the use of optimization for flight control laws clearance: a practical approach
On the use of optimization for flight control laws clearance: a practical approach
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关于飞行控制法间隙优化的使用：一种实用方法
DOI：
10.3182/20110828-6-it-1002.00462
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
G. Puyou
中科院分区：
文献类型：
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作者：
Rafael Fernandes de Oliveira;G. Puyou
摘要 随着最近人们对使用优化来解决棘手的工程问题的兴趣，本文研究了将多目标优化方法应用于飞行控制律（FCL）的间隙阶段。间隙问题涉及证明飞机控制系统能够使其在飞行包线范围内飞行，无论飞行员输入和环境条件如何。以前的工作重点关注这种方法的技术可行性，处理单一标准清除问题，但在实际验证阶段，通常需要检查多个标准，因此需要多次运行单目标方法，或者理想情况下需要多目标方法。在这项工作中，著名的非支配排序遗传算法（NSGA-II）和内部开发的方法都针对高度非线性认证的空客飞机仿真模型进行了测试。与当前的概率蒙特卡罗方法相比，找到多标准间隙问题的帕累托最优解的概率更高。还评估了局部和全局优化的混合组合，以改善最坏的情况。
Abstract Following the recent interest of using optimization to solve tricky engineering problems, this paper studies the use of multi-objective optimization methods applied to the clearance phase of flight control laws (FCL). The clearance problem is related to prove that aircraft control systems are capable of keeping it flying within flight envelope bounds, regardless of pilot inputs and environmental conditions. Previous work have focused on the technical viability of this approach, dealing with single criterion clearance, but in an real validation phase, there are usually multiple criteria that need to be checked, therefore requiring either multiple runs of a single-objective method, or ideally a multi-objective approach. In this work, the well-known Non-dominated Sorting Genetic Algorithm (NSGA-II) and a internally developed method are both tested against a highly non-linear certified AIRBUS aircraft simulation model. Pareto optimal solutions to the multi-criteria clearance problem were found with higher probability than the current probabilistic Monte-Carlo approach. A hybrid combination of local and global optimization is also evaluated to improve worst-cases.