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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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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