Research On Performance Monitoring And Performance Degradation Mitigation Control Of A Turbofan Engine

Aeroengine performance degradation mitigation control is aimed at the performance degradation of core components of the air path due to wear,corrosion,etc.during the operation of the engine.Through the real-time estimation of key air path health parameters and the controller design,the engine performance is fully explored to meet the demand of the aircraft for the engine thrust.This paper is based on the key project of a Ministry,"The Research on Fundamental Problems of XX Engine",focuses on the performance monitoring and the performance degradation mitigation control of a certain kind of turbofan engine.The main contents of this paper include:Firstly,the component-level model of the engine is constructed based on the aerodynamics thermodynamics of the turbofan engine.On this basis,the partial derivative method and the least squares fitting method are used to establish the state variable model at the steady-state point of the engine.Compared with the engine nonlinear model,the established state variable model has higher accuracy,which lays a foundation for the subsequent establishment of onboard adaptive model of aeroengine.Secondly,an improved Kalman filter algorithm based on the stochastic configuration network is proposed to solve the problem that the accuracy of the on-board adaptive model based on Kalman filter decreases when the number of sensors is less than the state variables to be measured in the turbofan engine performance monitoring.Firstly,an estimator of engine gas path health parameters based on stochastic configuration network is designed.Furthermore,the estimation results of health parameters by the stochastic configuration network are taken as penalty terms and added into the objective function of a posteriori estimation of the improved Kalman filter algorithm,which realizes the fusion of the estimation results of the improved Kalman filter and the estimation results of the stochastic configuration network.Finally,the firefly algorithm is used to optimize the structural parameters in order to solve the problem of the insufficient estimation accuracy of individual health parameters and improve the universality.The simulation results show that the root mean square error of the estimation results based on the improved Kalman filter with stochastic configuration network decreases by 63.14% on average,and the average error decreases by 67.79% on average.Therefore,the proposed method can achieve the accurate estimation of engine performance degradation and improve the accuracy of the on-board adaptive model.Finally,in order to realize that the aeroengine can still meet the thrust demand in the performance degradation state,a method of engine performance degradation mitigation controller based on neural network was proposed.Firstly,an indirect thrust estimator was designed to estimate the unmeasurable thrust under the condition of engine performance degradation.By using the unmeasurable health parameters obtained from the improved Kalman filter based on the proposed stochastic configuration network,the on-board adaptive model of the engine was updated in real time to obtain the estimated output thrust.Furthermore,the linear active disturbance rejection controller is designed as the inner loop controller and the generalized regression neural network controller is designed as the outer loop controller,so that the engine can still output the expected thrust under the condition of performance degradation.Finally,the proposed method is validated by a hardware-in-loop verification platform.Simulation results show that the proposed method can effectively achieve thrust recovery under performance degradation conditions.In the case of the single degradation,the average thrust error is 0.342%.In the case of multiple degradation,the average thrust error is 0.677%,and the dynamic adjustment time is about 3s,with good dynamic characteristics,to meet the real-time requirements of the control system.