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Full-state-constrained intelligent adaptive control for nonlinear systems with unmodeled dynamics and mismatched disturbances

Published online by Cambridge University Press:  06 December 2024

Y. Yin ,
X. Ning ,
Z. Wang Open the ORCID record for Z. Wang [Opens in a new window]  and
R. Li
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
X. Ning
Affiliation:
National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, China School of Astronautics, Northwestern Polytechnical University, 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
R. Li
Affiliation:
AECC South Industry Company Limited, Zhuzhou, China
*
Corresponding author: Z. Wang; Email: wz_nwpu@126.com
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Abstract

This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncertain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key point of the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled with other states of the system under the conditions of various disturbances and full state constraints. Firstly, to guarantee that the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transformation method has been proposed, which can simplify the constrained control problem of the system states into a bounded control problem of the transformed states. Secondly, considering the coupling relationship between the unmodeled dynamics and other states of the controlled system such as system states and control inputs, a decoupling approach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radial basis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeled dynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatched disturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIAC strategy.

Keywords

adaptive neural network controlfull-state constraintsmismatched disturbanceunmodeled dynamics
Type
Research Article
Information
The Aeronautical Journal , Volume 129 , Issue 1335 , May 2025 , pp. 1286 - 1302
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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