Genetic algorithm LQG and neural network controllers for gust response alleviation of flying wing unmanned aerial vehicles

IF 3.4 2区 工程技术 Q1 ENGINEERING, MECHANICAL Journal of Fluids and Structures Pub Date : 2024-10-07 DOI:10.1016/j.jfluidstructs.2024.104199
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Abstract

In this paper, a genetic algorithm linear quadratic Gaussian controller (GA-LQG) and an artificial neural network (ANN) controller are implemented for gust response alleviation of lightweight flying wings undergoing body-freedom oscillations. A state–space aeroelastic model has been formulated by coupling the unsteady vortex lattice method for aerodynamics with finite-element based structural dynamics. The model is subsequently reduced using balanced truncation to improve computational efficiency during controller synthesis. Open-loop simulations show that the flying wing experiences large changes in pitching angles during gusts. For GA-LQG controller, the LQG weights are optimised using a genetic algorithm, maximising a defined fitness function. Generally, the GA-LQG controller reduces the plunge displacements by up to 94.2% while damping out wingtip displacements for discrete and continuous gusts. Similarly, the ANN controller effectively regulates both the plunge displacements and wingtip displacements, including gust cases that are not presented during the ANN training phase. The ANN controller is more effective in correcting wingtip displacements during discrete gusts than the GA-LQG controller, while the opposite is true for the continuous gust cases. The ANN controller offers several advantages over the GA-LQG controller, including the elimination of the need for a Kalman filter for full state estimation and offers a non-linear control solution.
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用于减轻飞翼无人飞行器阵风响应的遗传算法 LQG 和神经网络控制器
本文采用遗传算法线性二次高斯控制器(GA-LQG)和人工神经网络控制器(ANN)来减轻轻质飞翼在体自由度振荡下的阵风响应。通过将空气动力学的非稳态涡流晶格法与基于有限元的结构动力学相结合,建立了一个状态空间气动弹性模型。随后采用平衡截断法对该模型进行缩减,以提高控制器合成过程中的计算效率。开环仿真显示,飞行翼在阵风时俯仰角变化很大。对于 GA-LQG 控制器,LQG 权重是通过遗传算法优化的,最大化定义的适应度函数。一般来说,GA-LQG 控制器可减少高达 94.2% 的俯仰位移,同时抑制离散和连续阵风的翼尖位移。同样,ANN 控制器也能有效调节垂尾位移和翼尖位移,包括在 ANN 训练阶段未出现的阵风情况。与 GA-LQG 控制器相比,ANN 控制器能更有效地修正离散阵风时的翼尖位移,而在连续阵风情况下则相反。与 GA-LQG 控制器相比,ANN 控制器具有多项优势,包括无需使用卡尔曼滤波器进行全状态估计,并提供了非线性控制解决方案。
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来源期刊
Journal of Fluids and Structures
Journal of Fluids and Structures 工程技术-工程:机械
CiteScore
6.90
自引率
8.30%
发文量
173
审稿时长
65 days
期刊介绍: The Journal of Fluids and Structures serves as a focal point and a forum for the exchange of ideas, for the many kinds of specialists and practitioners concerned with fluid–structure interactions and the dynamics of systems related thereto, in any field. One of its aims is to foster the cross–fertilization of ideas, methods and techniques in the various disciplines involved. The journal publishes papers that present original and significant contributions on all aspects of the mechanical interactions between fluids and solids, regardless of scale.
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