系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (2): 703-714.doi: 10.12305/j.issn.1001-506X.2024.02.34

• 制导、导航与控制 • 上一篇    

随机参数摄动下的高超声速飞行器姿态控制

刘正洋1, 周丽1,2,*, 张瑞1   

  1. 1. 南京信息工程大学自动化学院, 江苏 南京 210044
    2. 江苏省大气环境与装备技术协同创新中心, 江苏 南京 210044
  • 收稿日期:2023-01-16 出版日期:2024-01-25 发布日期:2024-02-06
  • 通讯作者: 周丽
  • 作者简介:刘正洋 (1996—), 男, 硕士研究生, 主要研究方向为飞行器控制、预测控制
    周丽 (1976—), 女, 副教授, 博士, 主要研究方向为非线性控制、智能控制
    张瑞 (1995—), 男, 硕士研究生, 主要研究方向为移动机器人控制
  • 基金资助:
    国家自然科学基金(61573190)

Attitude control of hypersonic vehicle with random parameter perturbations

Zhengyang LIU1, Li ZHOU1,2,*, Rui ZHANG1   

  1. 1. School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
    2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China
  • Received:2023-01-16 Online:2024-01-25 Published:2024-02-06
  • Contact: Li ZHOU

摘要:

针对高超声速飞行器随机参数摄动的姿态控制问题, 提出一种基于轨迹线性化和扩展卡尔曼滤波(extended Kalman filter, EKF)的预测滑模控制方法。首先, 针对随机参数摄动的高超声速飞行器非线性模型, 采用轨迹线性化方法建立线性时变误差调节模型。通过分析参数误差的统计特性, 将统计信息以参数协方差矩阵的形式进行表示, 并设计EKF对受扰误差状态进行滤波。然后, 采用预测滑模控制方法设计误差稳定调节器, 使系统快速趋于稳定。所提方法对随机参数摄动和非线性干扰都具有强鲁棒性。仿真验证了所提方法的有效性。

关键词: 高超声速飞行器, 随机参数摄动, 轨迹线性化, 扩展卡尔曼滤波, 预测滑模

Abstract:

In view of the attitude control problem of a hypersonic vehicle with range of random parameter perturbations a predictive sliding mode control method based on trajectory linearization and extended Kalman filter (EKF) is proposed. Using trajectory linearization method, a linear time-varying error adjustment model is established from the nonlinear model of hypersonic vehicle. The statistical information in the linearized model caused by parameter perturbations is expressed in the form of parameter covariance matrix by analysis of the statistical characteristics of parameter errors, then the EKF is designed to filter the disturbed error state. Finally, the predictive sliding mode control method is used to design the error stabilization regulator to realize the rapid stabilization of the system. The proposed method is robust to both nonlinear disturbances and random parameter perturbations. Simulation results verify the effectiveness of the proposed method.

Key words: hypersonic aircraft, random parameter perturbation, trajectory linearization control, extended Kalman filter (EKF), predicted sliding mode

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