Aero-engine gas path parameter online prediction method

Abstract

The invention discloses an aero-engine gas circuit parameter online prediction method. The method comprises the steps: A, obtaining an engine measurable parameter measurement value at the current moment k through a sensor; B, performing incremental estimation on the health parameters and the state quantity of the engine by using a linear Kalman filter; C, updating health parameters and external input of the engine model, and updating a state space model and a gas path parameter prediction state space model required by the Kalman filter based on the component-level partial derivative model; andD, transmitting the control input sequence to the gas circuit parameter prediction state space model, and conducting online prediction on engine gas circuit parameter changes. According to the method, the real engine health state can be effectively evaluated in the dynamic process, and the gas path parameter change can be quickly and accurately predicted on line according to the given input sequence.

Description

technical field [0001] The invention belongs to the field of system simulation and control in aerospace propulsion theory and engineering, and in particular relates to an online prediction method for air path parameters of an aeroengine. Background technique [0002] With the development and progress of engine technology, higher requirements are put forward for the control system of aero-engine, which makes the model-based control method with the potential of mining engine performance attract much attention. Model-based control uses the onboard model to estimate and predict the output parameters of the engine gas path, and provides the engine state change information for the control system to achieve high-performance control. [0003] In the application process of the model-based control method based on the optimization idea, it is necessary to predict the output sequence corresponding to the controlled quantity and the safety limit quantity under multiple sets of given futu...

AI Technical Summary

Problems solved by technology

However, due to its complex structure and large amount of calculation, repeated calls to this model during the optimization process will generate a huge demand for the computing power of the airborne system

In addition, the linearization operation for Kalman filter estimation will generate additional model calculation requirements, so it is difficult to meet the real-time requirements of model-based control based on optimization ideas

[0009] Therefore, the existing online gas path parameter prediction method is limited by the shortcomings of the airborne model it uses, and it is difficult to meet the application requirements of model-based control based on optimization ideas. A method with high precision, high real-time performance and low computational burden is needed On-line Gas Path Parameter Prediction Method

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