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A Comparative Study on Multidisciplinary and Multi-objective Optimal Control Design of an Aircraft Wing with Multiple Aileron

机译:多学科和多目标优化控制设计的比较研究

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This paper presents a multidisciplinary and multiobjective optimal design of an aircraft wing with two, three, and four control surfaces. The study aims to compare the performance of the wing in terms of aerodynamic loads rejection, stability robustness, and energy consumption. An LQR (Linear Quadratic Regulator) is designed for each control surface. The geometrical parameters of the control surfaces such as the span-wise and chord lengths, and the diagonal elements of the LQR weighting matrices are optimally adjusted by the NSGA-II (Non-dominated Sorting Genetic Algorithm). The algorithm returns a set of solutions called the Pareto set and its function evaluation forms another set known as the Pareto front. The solution set holds optimal geometrical and control decision variables that produce various degrees of optimal trade-offs among the design goals. To facilitate the comparison between the three optimization problems, a post-processing algorithm that operates on the Pareto front is utilized. Then, the knee points and portions of the Pareto fronts are compared. The optimal solutions show that there are conflicting relationships between the design objectives. The disturbance rejection of the wing with the two ailerons is the least effective however control energy consumption is the smallest as compared to the other configurations. The wing with the three ailerons at 18 different design options has the best relative stability. At the knee point, a wing having four control surfaces can offer the best disturbance rejection but at the expense of the control energy. With these considerations, a wing with three surfaces can be the best compromise among the other configurations.
机译:本文介绍了具有两个,三个和四个控制表面的飞机翼的多学科和多目标最佳设计。该研究旨在比较机翼在空气动力学负载抑制,稳定性鲁棒性和能量消耗方面的性能。 LQR(线性二次调节器)专为每个控制表面设计。由NSGA-II(非主导分类遗传算法)最佳地调整控制表面的控制表面的几何参数,以及LQR加权矩阵的对角线元件(非主导的分类遗传算法)。该算法返回一个名为Pareto Set的一组解决方案,其函数评估形成了另一种称为Paroto Front的集合。解决方案集可容纳最佳几何和控制决策变量,这些变量在设计目标之间产生各种最佳权衡。为了便于三种优化问题之间的比较,利用在帕累托前部运行的后处理算法。然后,比较膝部点和帕累托前部的部分。最佳解决方案表明,设计目标之间存在冲突的关系。与两个jilerons的机翼的扰动抑制是最不有效的,然而控制能量消耗与其他配置相比最小。具有18个不同设计选项的三个jilerons的机翼具有最佳的相对稳定性。在膝盖点,具有四个控制表面的机翼可以提供最佳的扰动排斥,但是为控制能量为代价。通过这些考虑,具有三个表面的机翼可以是其他配置中的最佳折衷。

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