Data-driven predictive control for hypersonic morphing vehicle using composite observer
HE Tao
CHEN Zhong
CEN Li-hui
LIAO Yu-xin
Abstract：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.
Keywords：hypersonic morphing vehicleKoopman operatorcomposite observermodel predictive control
Publication Date：2025-12-30
Online Publishing Date：2026-03-05(First online date of this platform, not the publication date of the document)
Pages：10( 2535-2544 )