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Neural adaptive distributed tight formation control for multiple hypersonic gliding vehicles with mismatched uncertainties and multisource external disturbances

Published online by Cambridge University Press:  18 July 2025

X. Xing ,
W. Li ,
Z. Wang Open the ORCID record for Z. Wang [Opens in a new window] ,
Y. Bai ,
S. Wang  and
X. Ning
X. Xing
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China
W. Li
Affiliation:
Shanghai Satellite Engineering Research Institute, Shanghai, 201100, China
Z. Wang*
Affiliation:
Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China Northwest Institute of Mechanical and Electrical Engineering, Xianyang, 712099, China
Y. Bai
Affiliation:
China Academy of Launch Vehicle Technology, Beijing, 100076, China
S. Wang
Affiliation:
China Academy of Launch Vehicle Technology, Beijing, 100076, China
X. Ning
Affiliation:
School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China
*
Corresponding author: Z. Wang; Email: wangzheng0905@nwpu.edu.cn
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Abstract

This paper studies the adaptive distributed consensus tracking control framework for hypersonic gliding vehicles (HGVs) flying in tight formation. The system investigated in this paper is non-affine and subjected to multisource disturbances and mismatched uncertainties caused by a dramatically changing environment. Firstly, by refining the primary factors in the three-dimensional cluster dynamics, a non-affine closed-loop control system is summarised. Note that actual control is coupled with states, an additional auxiliary differential equation is developed to introduce additional affine control inputs. Furthermore, by employing the hyperbolic tangent function and disturbance boundary estimator, time-varying multisource disturbances can be handled. Several radial base function neural networks (RBFNNs) are utilised to approximate unknown nonlinearities. Furthermore, a generalised equatorial coordinate system is proposed to convert the longitudinal, lateral and vertical relative distances in the desired formation configuration into first-order consensus tracking error, such as latitude, longitude and height deviations. Analysis based on the Lyapunov function illustrates that variables are globally uniformly bounded, and the output tracking error of followers exponentially converges to a small neighbourhood. Finally, numerical simulations of equilibrium glide and spiral diving manoeuvers are provided to demonstrate the validity and practicability of the proposed approach.

Keywords

distributed tight formation controlmulti-HGV systemnon-affineradial base function neural network

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Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 25
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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