基于双层平衡流形展开模型的涡扇发动机系统辨识OA

To address the applicability issues of traditional single-input system identification methods in turbofan engine control and fault diagnosis,particularly their limitations in handling highly nonlinear dynamic characteristics and multiple input-output relationships,this paper proposes an input dimension expansion method based on similarity theory and affine structure.By integrating the reduced-order equilibrium manifold expansion(EME)model with the multi-cell equilibrium manifold expansion model,this method enables multi-input modeling within the framework of a single-input equilibrium manifold expansion model.The effectiveness of the proposed dual-layer equilibrium manifold expansion model is validated through theoretical analysis and simulation experiments.The results demonstrate that the maximum error of the proposed model is only 1.309％,significantly lower than that of other models,while its average error range is merely one-fourth of that of the NARX network model.Compared to traditional nonlinear models(such as Wiener,Hammerstein,Hammerstein-Wiener,and NARX network models),the proposed model exhibits superior accuracy and computational stability.This study lays a crucial foundation for future model-based research on fault detection,isolation,and control in turbofan engines.