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Three-dimensional finite-time guidance law based on sliding mode adaptive RBF neural network against a highly manoeuvering target

Published online by Cambridge University Press:  10 January 2022

G. Wu Open the ORCID record for G. Wu [Opens in a new window] ,
K. Zhang  and
Z. Han
G. Wu*
Affiliation:
School of Astronautics, Northwestern Polytechnical University, Xi’an, China
K. Zhang
Affiliation:
School of Astronautics, Northwestern Polytechnical University, Xi’an, China
Z. Han
Affiliation:
School of Astronautics, Northwestern Polytechnical University, Xi’an, China
*
E-mail: wugang88@mail.nwpu.edu.cn
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Abstract

In order to intercept a highly manoeuvering target with an ideal impact angle in the three-dimensional space, this paper promises to probe into the problem of three-dimensional terminal guidance. With the goal of the highly target acceleration and short terminal guidance time, a guidance law, based on the advanced fast non-singular terminal sliding mode theory, is designed to quickly converge the line-of-sight (LOS) angle and the LOS angular rate within a finite time. In the design process, the target acceleration is regarded as an unknown boundary external disturbance of the guidance system, and the RBF neural network is used to estimate it. In order to improve the estimation accuracy of RBF neural network and accelerate its convergence, the parameters of RBF neural network are adjusted online in real time. At the same time, an adaptive law is designed to compensate the estimation error of the RBF neural network, which improves the convergence speed of the guidance system. Theoretical analysis demonstrates that the state and the sliding manifold of the guidance system converge in finite time. According to Lyapunov theory, the stability of the system can be guaranteed by online adjusting the parameters of RBF neural network and adaptive parameters. The numerical simulation results verify the effectiveness and superiority of the proposed guidance law.

Keywords

Highly manoeuvering targetsFinite-time convergenceRBF neural networkTerminal sliding modeAdaptive law
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1301 , July 2022 , pp. 1124 - 1143
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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