Intelligent Anti-disturbance Control of Flight Vehicle for Variable-sweep Process

被引：0

作者：

Lu, Jiale ^{[1
]}

Liang, Xiaohui ^{[1
]}

机构：

[1] School of Automation, Northwestern Polytechnical University, Xi’an

来源：

Yuhang Xuebao/Journal of Astronautics | 2024年 / 45卷 / 12期

关键词：

Adaptive dynamic programming; Anti-disturbance control; Disturbance observer; Neural network; Variable-sweep flight vehicle;

D O I：

10.3873/j.issn.1000-1328.2024.12.012

中图分类号：

学科分类号：

摘要：

To address the issue of decreased tracking performance of the control system caused by complex aerodynamic changes during the morphing process of variable-sweep flight vehicle,an active disturbance rejection control method based on action-dependent heuristic dynamic programming is proposed. This method ensures the stability of the morphing process while optimizing the tracking accuracy of the system. Initially,a basic controller utilizing adaptive neural networks is designed to handle model uncertainties and severe external disturbances during the deformation process. This controller,based on disturbance observation and backstepping control method,ensures stable control of the flight vehicle during the morphing process. Subsequently,the tracking error of the basic control system is used as the state input for an action-critic network,which conducts online learning to approximate the optimal compensation control input. This approach eliminates the impact of aerodynamic changes and unmodeled dynamics on tracking performance,thereby enhancing the overall tracking performance of the closed-loop control system of the variable-sweep flight vehicle. Simulation analysis is conducted to validate the effectiveness and superiority of the proposed control method. © 2024 Chinese Society of Astronautics. All rights reserved.

引用

页码：1997 / 2008

页数：11

共 23 条

[11]

DONG Chaoyang, LIU Chen, WANG Qing, Et al., Near space vehicle control with tracking performance constraints, Journal of Astronautics, 40, 2, pp. 174-181, (2019)

[12]

YUAN L H, WANG L D, XU J T., Adaptive fault-tolerant controller for morphing aircraft based on the L2 gain and a neural network, Aerospace Science and Technology, 132, (2023)

[13]

MU C X, NI Z, SUN C Y, Et al., Air-breathing hypersonic vehicle tracking control based on adaptive dynamic programming [J], IEEE Transactions on Neural Networks and Learning Systems, 28, 3, pp. 584-598, (2017)

[14]

LIANG Xiaohui, HU Changhua, ZHOU Zhijie, Et al., ADP-based intelligent attitude fault-tolerant control for launch vehicles, Acta Aeronautica et Astronautica Sinica, 42, 4, (2021)

[15]

GUO Y Y, XU B., Finite-time deterministic learning command filtered control for hypersonic flight vehicle[J], IEEE Transactions on Aerospace and Electronic Systems, 58, 5, pp. 4214-4225, (2022)

[16]

GONG L G, WANG Q, DONG C Y, Et al., Prescribed performance control of morphing aircraft based on switched nonlinear systems and reinforcement learning[J], Measurement and Control, 52, 5-6, pp. 608-624, (2019)

[17]

GONG L G, WANG Q, HU C H, Et al., Switching control of morphing aircraft based on Q-learning[J], Chinese Journal of Aeronautics, 33, 2, pp. 672-687, (2020)

[18]

KIM S H, LEE H N, KIM Y., Continuous-time deterministic policy gradient-based controller for morphing aircraft without exploration, 2020 28th Mediterranean Conference on Control and Automation (MED), (2020)

[19]

WU Z H, LU J C, RAJPUT J, Et al., Adaptive neural control based on high order integral chained differentiator for morphing aircraft [J], Mathematical Problems in Engineering, 2015, (2015)

[20]

YOUNG W H., On classes of summable functions and their Fourier Series[J], Proceedings of the Royal Society of London Series A,Containing Papers of a Mathematical and Physical Character, 87, 594, pp. 225-229, (1912)

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