Aerodynamic modeling using an end-to-end learning attitude dynamics network for flight control

Zhao, Tun; Gong, Chen; Xiao, Wang; Yong, Enmi; Qian, Weikang

doi:10.1007/s10409-021-01151-6

Cited by 4 publications

(1 citation statement)

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“…The physics equations are incorporated into the loss function of a neural network to constrain the model while training, thereby ensuring outputs follow known physical laws [19]. Zhao et al [20] proposed an identification method of aerodynamic models using a physics neural network that incorporates the attitude dynamics of an aircraft. Li et al [21] utilized a PINN model to predict parameters of the state-space model using neural networks instead of predicting aerodynamics directly.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Feature Fusion Model for Unsteady Aerodynamic Modeling at High Angles of Attack

Dong,

Wang,

Lin

et al. 2024

Aerospace

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Unsteady aerodynamic prediction at high angles of attack is of great importance to the design and development of advanced fighters. In this paper, a weighted feature fusion model (WFFM) that combines the state-space model and neural networks is proposed to build an unsteady aerodynamic model for the precise simulation and control of post-stall maneuvers. In the proposed model, the influences of the physical model on neural networks are considered and adjusted by introducing a standardization layer and a new weighting method. A long short-term memory (LSTM) network is used to fuse two mappings: one from flight states to aerodynamic loads, and the other from low-fidelity data to high-fidelity data. Data from wind tunnel oscillation experiments at high angles of attack using a new kind of wire-driven parallel robot and the traditional tail support are used for verifying the proposed aerodynamic model. The output of the WFFM is also compared with predictions from other models, such as the state-space model, single LSTM model, and feature fusion model not including a feature weighting layer. Results demonstrate improved accuracy of the proposed model in the interpolation and extrapolation tests. Furthermore, the WFFM is applied to the flight simulation of F-16 with different control inputs. Compared with conventional models, the WFFM shows improved accuracy and better generalization capability.

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Section: Introductionmentioning

confidence: 99%