引用本文:何涛,陈中,岑丽辉,廖宇新.基于复合观测器的高超声速变体飞行器数据驱动预测控制[J].控制理论与应用,2025,42(12):2535~2544.[点击复制]
HE Tao,CHEN Zhong,CEN Li-hui,LIAO Yu-xing.Data-driven predictive control for hypersonic morphing vehicle using composite observer[J].Control Theory & Applications,2025,42(12):2535~2544.[点击复制]
基于复合观测器的高超声速变体飞行器数据驱动预测控制
Data-driven predictive control for hypersonic morphing vehicle using composite observer
摘要点击 102  全文点击 14  投稿时间：2024-08-25  修订日期：2025-10-22
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DOI编号  10.7641/CTA.2025.40455
  2025,42(12):2535-2544
中文关键词  高超声速变体飞行器  Koopman算子  复合观测器  模型预测控制
英文关键词  hypersonic morphing vehicle  Koopman operator  composite observer  model predictive control
基金项目  国家自然科学基金重大研究计划重点支持项目(92371203),装备预研教育部联合基金项目(8091B032134),湖南省自然科学基金青年基金项目(20 22JJ40633), 思源联盟开放基金项目(HTKJ2023KL012006)资助.
作者单位E-mail
何涛 中南大学自动化学院 234612126@csu.edu.cn 
陈中 中南大学自动化学院  
岑丽辉* 中南大学自动化学院 lhcen@csu.edu.cn 
廖宇新 中南大学自动化学院  
中文摘要
      针对受参数不确定性和外部干扰影响的高超声速变体飞行器姿态控制问题,本文提出了一种基于复合观 测器的数据驱动模型预测控制方法.为了解决高超声速变体飞行器建模与控制中的非线性问题,采用基于Koo pman算子理论的数据驱动建模方法,利用Autoencoder神经网络拟合Koopman算子的最优升维函数,并通过拓展动 态模式分解(EDMD)获得有限维空间截断的线性标称模型.此外,为了抑制干扰带来的不利影响,提出了一种同步 估计方法构建复合观测器,该观测器包括基于Koopman升维模型的Luenberger型观测器和同步干扰观测器;随后, 基于线性标称模型和干扰估计值设计了模型预测控制器,并且证明了其迭代可行性和输入输出稳定性;最后,通过 仿真验证了控制器的有效性和可行性.
英文摘要
      In order to address the attitude control problem for hypersonic morphing vehicle affected by parameter per turbation and external disturbances, a data-driven model predictive control (MPC) approach is developed based on the com posite observer in this paper. To tackle nonlinearities in aircraft modeling and control, this study introduces a data-driven modeling method based on the Koopman operator theory. An autoencoder neural network is employed to approximate the optimal lifting function of the Koopman operator, enabling the extraction of a linear nominal model on finite-dimensional lifting space using extended dynamic mode decomposition (EDMD). Additionally, to mitigate the adverse effects of dis turbances, a synchronous estimation scheme is proposed to construct a composite observer for hypersonic vehicles, which consists of a Luenberger-type observer based on Koopman lifting model and a synchronous disturbance observer. Sub sequently, a MPC controller is designed based on the linear nominal model and disturbance estimates. The recursive feasibility and the input-output stability of the controller are proven. Finally, simulation results validate the effectiveness and feasibility of the proposed controller.