Method for establishing dynamic and static aero-engine onboard model

Abstract

The invention discloses a method for establishing a dynamic and static aero-engine onboard model and belongs to the technical field of aero-engine control. Firstly, sample data compression is conducted through a similarity criterion and a taylor expansion principle so that the sample data size can be greatly reduced and time can be greatly shortened; then, a dynamic onboard model based on a sparse autocoder and a static onboard model based on a BP neural network are trained through dynamic data and static data in compressed sample data respectively; finally, corresponding quasi-steady-state judgment logic is set, the sparse autocoder dynamic model is used in the dynamic process, and the BP network static model is used in the static process. Compared with the prior art, the established aero-engine onboard model has higher precision under the dynamic condition and the static condition and is better in instantaneity and lower in requirement for data storage space.

Description

technical field [0001] The invention relates to the technical field of aero-engine control, in particular to a method for constructing an airborne model of an aero-engine in a dynamic and steady state. Background technique [0002] The United States launched the Integrated High Performance Turbo Engine Technology (IHPTET) program between 1988 and 2005, aiming to improve engine performance, reduce engine weight, and increase engine thrust-to-weight ratio. Among them, the advanced turbine engine control adopts the model-based intelligent engine control (IEC, Intelligent Engine Control) technology, which changes the traditional sensor-based control mode and reflects the actual working state of the engine online through the onboard adaptive model , calculate engine performance parameters such as thrust, power, surge margin, etc. as feedback quantities, and form a control loop of direct performance parameters to fully tap the engine potential. At the beginning of this century, a...

AI Technical Summary

Problems solved by technology

For example, using the neural network method to model the airborne model, first use the engine component-level model to sample points, and use the engine component-level model calculated by the N-R method to calculate a sample point for about 200-300 iterations, which takes about 5 seconds, and the sample input The quantity is 10 dimensions. If 10 points are required for each dimension to ensure that the sample is dense, it will take a total of 1.38×107 hours, which is obviously difficult to sample and even more difficult to train.

Images