A hybrid onboard adaptive model for aero-engine parameter prediction

An onboard aero-engine model with low computation burden and high accuracy is urgent for online parameter prediction in some model-based control applications. However, current engine models, including component level models, linear models and data-driven models cannot meet the demands of model-based control and diagnostics well. Thus, a novel hybrid adaptive model is proposed, which consists of a component level model, an improved incremental linearized Kalman filter and a state space model. The improved linearized Kalman filter is used to adapt the component level model to the performance degradations of the engine, and the state space model is implemented for online prediction with a low computation burden. Both the linearized Kalman filter and the state space model are obtained by linearizing the adaptive component level model in real-time. Thus, an exact derivative calculation method is extended and an improved transient calculation and linearization joint algorithm is proposed to achieve real-time property. Experiments are conducted to demonstrate the effectiveness and real-time property of proposed model in terms of estimation accuracy, prediction accuracy and time consumption. The results show that the proposed adaptive model can not only estimate the health parameters and track the performance degradations well but also predict interesting outputs over next several sample periods accurately and quickly. In addition, the proposed adaptive model can complete the estimation and prediction task in a sample period (25 ms) in a micro-controller unit, which suggests the great real-time property of the proposed model. (C) 2020 Elsevier Masson SAS. All rights reserved.