Research On Component Characteristics Prediction And Multivariable Control For A Turbofan Engine

With the development of aviation technology,the traditional single variable control can no longer meet the technical requirements of advanced aircrafts for engines in the future,and the research on multivariable control has become an important direction for the technological innovation of aircraft engines.Due to the changeable working environment and strong coupling of aero-engine,it is a key challenge to solve the anti-interference problem while realizing multi-variable reliable control.In addition,aero-engine model is the basis of control design and simulation,and component characteristics are the key to modeling.At present,at low rotational speed,the characteristics of rotating components are greatly affected by external disturbances,and it is difficult to obtain accurate component characteristics in bench test.The problem of engine performance difference caused by component characteristics difference under different working conditions also needs to be solved urgently.Based on a certain type of high-performance turbofan engine modeling and simulation and multivariable control research project,this paper studies the above problems based on a certain type of turbofan engine.The main contents are as follows:Aiming at the problem of difficult to obtain the characteristics of low speed rotating parts of engine,the mechanism method and data-based method are used to predict the low speed characteristics.Based on the similarity principle,the characteristics of low-speed components can be predicted.This method has mature theory but complex calculation process,and the prediction accuracy depends on the prediction index and the known characteristic data.Based on the BP neural network,the component characteristics can be predicted by establishing the mapping relationship between the component characteristics and the conversion speed.This method is fast and easy to implement,and the prediction accuracy is only dependent on the data amount of the known characteristics.However,as the prediction characteristics are far from the known characteristics,the accuracy gradually decreases.It is proved that the prediction results of the two methods are in line with the distribution law of component characteristics,and have strong practicability and universality.Aiming at the problem that the model accuracy is reduced due to the influence of the working environment on the engine component characteristics,a method based on the combination of experimental data and intelligent algorithm is adopted.The component characteristic parameters are corrected by the optimization algorithm to improve the overall accuracy of the model.Considering that the particle swarm optimization algorithm is easy to fall into local optimum,the idea of combining differential evolution algorithm and particle swarm optimization algorithm is proposed,and the PSO_DE algorithm of particle swarm hybrid difference algorithm is designed,which ensures the search speed under the premise of ensuring the global search optimization.Aiming at the problem of engine multivariable control,the design and verification of turbofan engine multivariable controller based on ADRC theory are realized.Through the simulation analysis of the tracking control of the two controlled variables of the high-pressure rotor speed and the total turbine pressure ratio under different working conditions,the results show that the designed controller can not only realize the accurate and reliable control of the engine in the steady state and transition state,but also has good anti-interference ability.