宇航学报 ›› 2021, Vol. 42 ›› Issue (10): 1237-1245.doi: 10.3873/j.issn.1000-1328.2021.10.005

• 制导、导航、控制与电子 • 上一篇    下一篇

运载火箭姿态系统自适应神经网络容错控制

马艳如,石晓荣,刘华华,梁小辉,王青   

  1. 1. 北京航空航天大学自动化科学与电气工程学院,北京 100191;2. 北京控制与电子技术研究所,北京 100038;3. 西北工业大学自动化学院,西安 710072

  • 收稿日期:2020-09-27 修回日期:2021-03-07 出版日期:2021-10-15 发布日期:2021-10-15
  • 基金资助:
    国家自然科学基金(61833016, 61873295);航空科学基金(2018ZA51003)

Adaptive Neural Network Fault Tolerant Control of  Launch Vehicle Attitude System

MA Yan ru, SHI Xiao rong, LIU Hua hua, LIANG Xiao hui, WANG Qing   

  1. 1.School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; 2. Beijing Institute of Control and Electronics Technology, Beijing 100038, China; 3. School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
  • Received:2020-09-27 Revised:2021-03-07 Online:2021-10-15 Published:2021-10-15

摘要: 针对运载火箭姿态系统跟踪问题,考虑干扰、执行器故障和模型不确定因素的影响,设计了一种基于自适应神经网络的非线性容错控制律。该控制算法结合了连续的终端滑模控制,径向基神经网络和自适应控制方法。首先,基于滑模控制理论,设计了一种快速终端滑模面,保证系统跟踪误差能够在有限时间收敛至零。然后,在终端滑模面基础上,提出了一种基于自适应径向基神经网络估计的终端滑模控制律。利用自适应参数的神经网络逼近系统参数并提高抗干扰性能,采用平滑连续控制策略消除了终端滑模中的颤动现象。通过李雅普诺夫的分析方法证明了闭环系统的收敛性和全局稳定性。采用数值仿真,验证了提出的基于自适应径向基神经网络的终端滑模控制律具有较好的跟踪性能和精度。


关键词: 运载火箭姿态跟踪系统, 执行器故障, 容错控制, 滑模控制, 自适应径向基神经网络

Abstract: In this paper, an adaptive neural network nonlinear fault tolerant control law is proposed for a launch vehicle attitude tracking system considering the influence of interference, actuator fault and model uncertainty. The control algorithm combines the continuous terminal sliding mode control, radial basis neural network and adaptive control methods. Firstly, based on the sliding mode control theory, a fast terminal sliding mode surface is designed to ensure that the system tracking error can converge to zero in a finite time. Then, based on the terminal sliding mode surface, a continuous terminal sliding mode control law based on the adaptive radial basis function neural network estimation is proposed. The neural network with adaptive parameters is used to approximate the system parameters and improve the anti interference performance, and the smooth continuous control strategy is used to eliminate the vibration phenomenon in the terminal sliding mode. The convergence and global stability of the closed loop system are proved by the Lyapunov’s analysis method. Using numerical simulation, it is verified that the terminal sliding mode control law based on the adaptive radial basis function neural network proposed has better tracking performance and accuracy.


Key words: Launch vehicle attitude tracking system, Actuator faults, Fault tolerant control, Sliding mode control, Adaptive radial basis neural network

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