An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

Liang, Ye; Li, Guangning; Xu, Mingfei; Zhao, Jun-min; Feng, Hui; Shi, Hongyan

doi:10.1016/j.dt.2023.07.012

Defence Technology

2023

DOI: 10.1016/j.dt.2023.07.012

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An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

Ye Liang

Guangning Li

Mingfei Xu

et al.

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2024

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Cited by 2 publications

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Numerical virtual flight investigation for longitudinal maneuver of a generic fighter based on machine learning

Yan,

Chang,

Wang

et al. 2024

Physics of Fluids

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Coupled with computational fluid dynamics (CFD), rigid body dynamics (RBD), and flight control system, the numerical virtual flight (NVF) technology can simulate the maneuvering flight process of an air vehicle under control. In this paper, the NVF investigation of longitudinal maneuvers with elevator and thrust vector control is performed for a generic fighter configuration. The rigid dynamic hybrid grid method is taken to realize the motion of the fighter, and the overlapping moving grid technology meets the deflection of the elevator. The Reynolds-averaged Navier–Stokes equations in arbitrary Lagrangian–Eulerian form are coupled with the RBD equations to solve aerodynamics and kinematics problems, while flight control is achieved through an advanced machine learning method. First, the fighter is forced to pitch with the periodic deflection of the elevator, and the unsteady computation is carried out to obtain aerodynamic data. Then, an artificial neural network (ANN) is adopted for aerodynamic identification and modeling, which involves establishing a model between the aerodynamic coefficient and pitching motion parameters. Afterward, the ANN-based NVF is implemented on the basis of the established model and deep reinforcement learning (DRL) is used to design the pitching control law of the fighter. The NVF results based on ANN show that the fighter has a good control effect under the action of the elevator, elevator with open-loop thrust vector, and elevator with closed-loop thrust vector, respectively, as well as the results from the CFD-based NVF system. Finally, the three-degree-of-freedom NVF based on CFD also indicates that the control law designed through DRL has good generalization characteristics. This study demonstrates the potential prospects of machine learning methods in the design and research for a novel generation of air vehicles.

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Numerical virtual flight investigation for longitudinal maneuver of a generic fighter based on machine learning

Yan,

Chang,

Wang

et al. 2024

Physics of Fluids

View full text Add to dashboard Cite

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Comparison of Machine Learning and Classic Methods on Aerodynamic Modeling and Control Law Design for a Pitching Airfoil

Yan,

Chang,

Wang

et al. 2024

International Journal of Aerospace Engineering

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Aerodynamic modeling and control law design methods are crucial foundational technologies that enable efficient maneuvering flight of aircraft. Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. To conduct this, a modified NACA0012 airfoil with a tail functioning as an elevator is employed as the geometry model. Through the utilization of the rigid dynamic grid method and overlapping grid technology, the pitch of the airfoil and the deflection of the elevator are efficient to execute. Firstly, the pitching moment coefficient is sampled through both steady and unsteady computations. Then, multivariate nonlinear regression (MNR) models and artificial neural network (ANN) models are established based on steady and unsteady sampling data, respectively. Additionally, the obtained model is evaluated using open‐loop control laws. Based on the evaluation, the proportional‐integral‐derivative (PID) control algorithm is used to design the airfoil pitching control law using the MNR model. Meanwhile, deep reinforcement learning (DRL) is used to design the pitching control law using the ANN model. Finally, the PID and DRL controllers are implemented in a CFD environment for airfoil pitching control to verify their effectiveness in application scenarios. The results suggest that models based on both steady and unsteady data can reflect dynamic aerodynamic characteristics. However, using unsteady computation for data sampling significantly reduces time consumption compared to steady computation. Furthermore, the model constructed by ANN may have unexpected excellent characteristics. Both the PID and DRL control laws, designed based on the models, perfectly complete the control process in the CFD environment. This study provides valuable insights for the implementation of controllable maneuvering flight in aircraft.

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An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

Cited by 2 publications

References 6 publications

Numerical virtual flight investigation for longitudinal maneuver of a generic fighter based on machine learning

Numerical virtual flight investigation for longitudinal maneuver of a generic fighter based on machine learning

Comparison of Machine Learning and Classic Methods on Aerodynamic Modeling and Control Law Design for a Pitching Airfoil

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