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In the process of robust controller design for aircraft engines, it is difficult to divide the flight envelope re-gion systematically. In this paper, the flight envelope division methods for aircraft engines based on thrust fuel con-sumption rate characteristics and dynamic pressure fuel consumption rate characteristics is proposed. According to the thrust, fuel consumption and dynamic pressure characteristics of a turbofan engine during steady-state operation within the full envelope, combined with the objective law of atmospheric conditions, the flight envelope is divided into 65 regions by two division methods, and the parameters of the surrounding small deviation regions and bound-ary points are replaced by the parameters of the corresponding nominal points of each region. By comparing the pa-rameters of nominal points and boundary points within the full envelope of the engine, the results show that both methods are effective for the full flight envelope division, which provides a theoretical basis for the subsequent air-craft engine controller design.

Aero-engine control system is an important mechanism of aircraft, the uncertainty of control gain attenuation and unmodeled dynamics of aero-engine can affect its control performance, therefore, a controller combining H∞ adaptive control and compensation control is designed.Firstly, the H∞ adaptive controller is designed based on the mixed sensitivity theory.And then, based on Lyapunov strict stability theory, the radial basis function (RBF)neural network compensation controller is designed to compensate the uncertainty, and the fitting speed is adjusted by the linear function related to the error.Finally, the normalized aero-engine model is taken as the controlled object to carry out the multi-variable simulation test.The results show that the adaptive controller designed in this paper can effectively compensate the uncertainty and reduce the overshoot and adjusting time compared with the H∞controller.

Accurate prediction of aviation material requirements for off-site missions is one of the main elements of a good trip assurance,therefore,the method combining gray relation analysis(GRA),improved particle swarm optimization(IPSO)algorithm and support vector machine(SVM)is proposed for predicting aviation material.Firstly,GRA is applied to analyze the factors influencing the demand for aviation materials carrying.Secondly,the particle swarm optimization algorithm is improved by introducing activity factors and non-linear inertia coefficients,and the SVM parameters are optimized by IPSO.Finally,the optimized SVM model is used to predict the demand for aviation materials.The results show that,the root mean square error predicted by aviation material prediction method based on GRA-IPSO-SVM is decreased by 0.16 than that of by using the method based on PSO-SVM,the mean absolute percentage error is decreased by 2.18%,and the prediction time is decreased by 0.7 s.