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Adaptive reinforcement learning control for a class of missiles with aerodynamic uncertainties and unmodeled dynamics

Published online by Cambridge University Press:  06 July 2023

X. Ning ,
S. Cao ,
B. Han ,
Z. Wang Open the ORCID record for Z. Wang [Opens in a new window]  and
Y. Yin Open the ORCID record for Y. Yin [Opens in a new window]
X. Ning
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China Science and Technology on Electromechanical Dynamic Control Laboratory, Xi’an, China School of Astronautics, Northwestern Polytechnical University, Xi’an, China
S. Cao
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China Science and Technology on Electromechanical Dynamic Control Laboratory, Xi’an, China Xi’an Institute of Electromechanical Information Technology, Xi’an, China
B. Han
Affiliation:
Xi’an Aeronautics Computing Technique Research Institute, Xi’an, China
Z. Wang*
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China Research Center for Unmanned System Strategy Development, Northwestern Polytechnical University, Xi’an, China Northwest Institute of Mechanical and Electrical Engineering, Xianyang, China Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China
Y. Yin
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China School of Astronautics, Northwestern Polytechnical University, Xi’an, China
*
Corresponding author: Z. Wang; Email: wz_nwpu@126.com
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Abstract

In this paper, a super-twisting disturbance observer (STDO)-based adaptive reinforcement learning control scheme is proposed for the straight air compound missile system with aerodynamic uncertainties and unmodeled dynamics. Firstly, neural network (NN)-based adaptive reinforcement learning control scheme with actor-critic design is investigated to deal with the tracking problems for the straight gas compound system. The actor NN and the critic NN are utilised to cope with the unmodeled dynamics and approximate the cost function that are related to control input and tracking error, respectively. In other words, the actor NN is used to perform the tracking control behaviours, and the critic NN aims to evaluate the tracking performance and give feedback to actor NN. Moreover, with the aid of the STDO disturbance observer, the problem of the control signal fluctuation caused by the mismatched disturbance can be solved well. Based on the proposed adaptive law and the Lyapunov direct method, the eventually consistent boundedness of the straight gas compound system is proved. Finally, numerical simulations are carried out to demonstrate the feasibility and superiority of the proposed reinforcement learning-based STDO control algorithm.

Keywords

straight air compound missile systemunmodeled dynamicsreinforcement learningsuper-twisting disturbance observer
Type
Research Article
Information
The Aeronautical Journal , Volume 128 , Issue 1320 , February 2024 , pp. 292 - 308
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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