基于机器学习的智能控制数值虚拟飞行方法

doi:10.7527/S1000-6893.2022.28098

Abstract

Abstract:

This paper proposes a method for numerical virtual flight with intelligent control based on machine learning. Combined with the case of the Basic Finner projectile model， the proposed algorithm is verified and evaluated. The results show the feasibility and good application prospect of the proposed algorithm. Firstly， a CFD/RBD coupled numerical virtual flight simulation model based on the overlapping dynamic mesh technology is constructed. According to the case of the Basic Finner projectile， the numerical simulation without control is conducted. Compared with the experimental data， the proposed numerical virtual flight simulation algorithm is verified and evaluated， showing that the numerical simulation algorithm can be used in the design and evaluation of the control parameters in the numerical virtual flight environment. Secondly， numerical simulations of the Basic Finner projectile’s pitch channel are carried out adopting the traditional PID control strategy and the intelligent PID control strategy， respectively. The PID intelligent controller based on the BP neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters. Compared with the traditional PID controller， the concerned control variable overshoot， rise time， transition time and steady-state error and other performance indicators have been significantly improved， and the higher learning efficiency leads to the faster system， larger overshoot， and smaller stability error.

Key words: numerical virtual flight, machine learning, BP neural network, PID, moving chimera grid

CLC Number:

V211.3

Yiming LIANG, Guangning LI, Min XU. Method for numerical virtual flight with intelligent control based on machine learning[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(17): 128098-81280986.

Figures/Tables 18

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Table 1

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References 35

1	阎超. 航空CFD四十年的成就与困境［J］. 航空学报， 2022， 43（10）： 526490.
	YAN C. Achievements and predicaments of CFD in aeronautics in past forty years［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（10）： 526490 （in Chinese）.
2	李周复. 风洞特种试验技术［M］. 北京：航空工业出版社， 2010：562-563.
	Li Z F. Special wind tunnel testing technology［M］. Beijing： Aviation Industry Press， 2010：562-563 （in Chinese）.
3	SALAS M D. Digital flight： The last CFD aeronautical grand challenge［J］. Journal of Scientific Computing， 2006， 28（2）： 479-505.
4	张来平，马戎，常兴华，等. 虚拟飞行中气动、运动和控制耦合的数值模拟技术［J］. 力学进展， 2014， 44（1）：376-417.
	ZHANG L P， MA R， CHANG X H， et al. Review of aerodynamics/kinematics/flight-control coupling methods in virtual flight simulations［J］. Advances in Mechanics， 2014， 44（1）：376-417 （in Chinese）.
5	SAHU J. Time-accurate numerical prediction of free flight aerodynamics of projectiles［C］∥ 2006 HPCMP Users Group Conference （HPCMP-UGC’06）. Piscataway： IEEE Press， 2007： 66-72.
6	COSTELLO M， GATTO S， SAHU J. Using CFD/RBD results to generate aerodynamic models for projectile flight simulation［C］∥ AIAA Atmospheric Flight Mechanics Conference and Exhibit. Reston： AIAA， 2007.
7	SAHU J， COSTELLO M， MONTALVO C. Development and application of multidisciplinary coupled computational techniques for projectile aerodynamics［C］∥ Seventh International Conference on Computational Fluid Dynamics. Hawaii： ICCFD， 2012： ICCF D7-4504.
8	KROLL N， ROSSOW C. Digital-X： DLR’s way towards the virtual aircraft［C］∥ NIA CFD Research. Virginia： DLR， 2012.
9	KROLL N， ABU-ZURAYK M， DIMITROV D， et al. DLR Project Digital-X： Towards virtual aircraft design and flight testing based on high-fidelity methods［J］. CEAS Aeronautical Journal， 2016， 7（1）： 3-27.
10	POST D， ATWOOD C， NEWMEYER K， et al. The computational research and engineering acquisition tools and environments （CREATE） program［C］∥ Computing in Science & Engineering. Piscataway： IEEE Press， 2015： 10-13.
11	POST D， ATWOOD C， NEWMEYER K， et al. The computational research and engineering acquisition tools and environments （CREATE） program［J］. Computing in Science & Engineering， 2016， 18（1）： 10-13.
12	杨云军，周伟江，崔尔杰. 耦合时间精度对模拟飞行器自由运动特性的影响［J］. 计算物理， 2007， 24（1）： 42-48.
	YANG Y J， ZHOU W J， CUI E J. Influence of coupling time accuracy on the simulation of aircraft free movement［J］. Chinese Journal of Computational Physics， 2007， 24（1）： 42-48 （in Chinese）.
13	袁先旭. 非定常流动数值模拟及飞行器动态特性飞行研究［D］. 绵阳：中国空气动力学研究与发展中心， 2002.
	YUAN X X. Numerical simulation for unsteady flows and research on dynamic characteristics of vehicle.［D］. Mianyang： China Aerodynamics Research and Development Center， 2002 （in Chinese）
14	李锋，杨云军，崔尔杰，等. 飞行器自激振荡的流动物理与动力学机制［J］. 空气动力学学报，2009， 27（z1）：106-113.
	LI F， YANG Y J， CUI E J， et al. Flow physics and dynamics mechanism of self-oscillation of an flight vehicle［J］. Acta Aerodynamica Sinica， 2009， 27（z1）： 106-113 （in Chinese）.
15	李锋，杨云军，刘周，等. 飞行器气动/飞行/控制一体化耦合模拟技术［J］. 空气动力学学报， 2015， 33（2）： 156-161.
	LI F， YANG Y J， LIU Z， et al. Integrative simulation technique of coupled aerodynamics and flight dynamics with control law on a vehicle［J］. Acta Aerodynamica Sinica， 2015， 33（2）： 156-161 （in Chinese）.
16	席柯，袁武，阎超，等. 基于闭环控制的带翼导弹虚拟飞行数值模拟［J］. 航空学报， 2014， 35（3）： 634-642.
	XI K， YUAN W， YAN C， et al. Virtual flight numerical simulation of the basic finner projectile with closed loop［J］. Acta Aeronautica et Astronautica Sinica， 2014， 35（3）： 634-642 （in Chinese）.
17	黄宇，阎超，席柯，等. 基于数值虚拟飞行技术的飞行器动态特性分析［J］. 航空学报， 2016， 37（8）：2525-2538.
	HUANG Y， YAN C， XI K， et al. Analysis of flying vehicle’s dynamic characteristics based on numerical virtual flight technology［J］. Acta Aeronautica et Astronautica Sinica， 2016， 37（8）： 2525-2538 （in Chinese）.
18	CHEN Q， CHEN J Q， XIE Y F， et al. Study and application of virtual flight simulation for rolling control of vehicles［J］. Journal of Computational Science， 2017， 21： 77-85.
19	WANG S， YAN C， WANG W. Virtual flight simulation of the basic finner projectile based on fuzz control［C］∥2017 8th International Conference on Mechanical and Aerospace Engineering （ICMAE）. Piscataway： IEEE Press， 2017： 494-497.
20	ZHANG L P， CHANG X H， MA R， et al. A CFD-based numerical virtual flight simulator and its application in control law design of a maneuverable missile model［J］. Chinese Journal of Aeronautics， 2019， 32（12）： 2577-2591.
21	陈翔，展京霞，陈科，等. 非定常气动力建模研究与虚拟飞行试验验证［J］. 实验流体力学， 2022， 36（3）： 65-72.
	CHEN X， ZHAN J X， CHEN K， et al. Unsteady aerodynamic modeling research and virtual flight test verification［J］. Journal of Experiments in Fluid Mechanics， 2022， 36（3）： 65-72 （in Chinese）.
22	ZHANG T H， KAHN G， LEVINE S， et al. Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search［C］∥ 2016 IEEE International Conference on Robotics and Automation （ICRA）. Piscataway： IEEE Press， 2016： 528-535.
23	HENNES D， IZZO D， LANDAU D. Fast approximators for optimal low-thrust hops between main belt asteroids［C］∥ 2016 IEEE Symposium Series on Computational Intelligence （SSCI）. Piscataway： IEEE Press， 2017： 1-7.
24	LI Q Y， QIAN J X， ZHU Z N， et al. Deep neural networks for improved， impromptu trajectory tracking of quadrotors［C］∥ 2017 IEEE International Conference on Robotics and Automation （ICRA）. Piscataway： IEEE Press， 2017： 5183-5189.
25	谢亮，徐敏，安效民，等. 基于径向基函数的网格变形及非线性气动弹性时域仿真研究［J］. 航空学报， 2013， 34（7）： 1501-1511.
	XIE L， XU M， AN X M， et al. Research of mesh deforming method based on radial basis functions and nonlinear aeroelastic simulation［J］. Acta Aeronautica et Astronautica Sinica， 2013， 34（7）： 1501-1511 （in Chinese）.
26	李广宁. 全机实用外形绕流Navier-Stokes方程数值模拟及其软件开发研究［D］. 西安：西北工业大学， 2011.
	LI G N. Numerical simulations of Navier-Stokes flow field about full aircraft and the development of CFD software［D］. Xi’an： Northwestern Polytechnical University， 2011 （in Chinese）.
27	姚伟刚. 非线性气动弹性系统时域仿真研究［D］. 西安：西北工业大学， 2010.
	YAO W G. Nonlinear aeroelastic system simulation in time domain［D］. Xi’an： Northwestern Polytechnical University， 2010 （Chinese）.
28	蒋胜矩，梁益铭. 一种基于数值虚拟飞行技术的弹丸稳定性评估方法［J］. 弹箭与制导学报， 2017， 37（1）： 121-124， 128
	JIANG S J， LIANG Y M. A estimation method for projectile stability based on numerical virtual flight technology［J］. Journal of Projectiles， Rockets， Missiles and Guidance， 2017， 37（1）： 121-124， 128 （in Chinese）
29	梁益铭，康顺. 旋转弹箭数值虚拟飞行研究［J］. 弹箭与制导学报， 2017， 37（1）： 107-111
	LIANG Y M， KANG S. Research on numerical virtual flight of spinning projectile［J］. Journal of Projectiles， Rockets， Missiles and Guidance， 2017， 37（1）： 107-111 （in Chinese）
30	CHAKRAVARTHY S， GOLDBERG U， PEROOMIAN O， et al. Some algorithmic issues in viscous flows explored using a unified-grid CFD methodology［C］∥ 13th Computational Fluid Dynamics Conference. Reston： AIAA， 1997： 1944.
31	DOUGHERTY F C， KUAN J H. Transonic store separation using a three-dimensional chimera grid scheme［R］. Reston： AIAA， 1989.
32	罗炯，李志宏，陈科，等. 基于嵌套网格变几何轴对称进气道非定常数值模拟［J］. 航空学报， 2022， 43（12）： 627028.
	LUO J， LI Z H， CHEN K， et al. Unsteady numerical simulation of variable geometry axisymmetric inlet based on overset grid［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（12）： 627028 （in Chinese）.
33	陈浩，袁先旭，王田天，等. 国家数值风洞（NNW）工程中的黏性自适应笛卡尔网格方法研究进展［J］. 航空学报， 2021， 42（9）： 625732.
	CHEN H， YUAN X X， WANG T T， et al. Research progress of viscous adaptive Cartesian grid method in national numerical wind tunnel （NNW） project［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（9）： 625732 （in Chinese）.
34	DUPUIS A D， HATHAWAY W. Aeroballistic range tests of the basic finner reference projectile at supersonic velocities［R］. Valcatier： Defense Research Establishment， 1997.
35	ALAIN DUPUIS. Aeroballistic range and wind tunnel tests of the basic finner reference projectile from subsonic to high supersonic velocities［R］. Valcatier： Defense Research Establishment， 2002.

幅值/（°）	方式	超调量σ_p/%	上升时间t_r/s	过渡时间t_s/s	稳态误差/%
5	传统PID	17.04	0.091	0.515	0.46
5	智能PID	0.78	0.034	0.050	0.16

15	传统PID	13.40	0.089	0.562	0.38
15	智能PID	1.11	0.067	0.089	0.12

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Method for numerical virtual flight with intelligent control based on machine learning

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