Aero-engine direct thrust control method based on nonlinear model prediction control

A nonlinear model and aero-engine technology, applied in the direction of engine control, engine function, engine components, etc., can solve problems such as the decline in thrust control accuracy, and achieve the effect of improving control accuracy and response speed

Active Publication Date: 2019-09-10
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, the corresponding relationship between the measurable parameters and the engine thrust or pow

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  • Aero-engine direct thrust control method based on nonlinear model prediction control
  • Aero-engine direct thrust control method based on nonlinear model prediction control
  • Aero-engine direct thrust control method based on nonlinear model prediction control

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Embodiment Construction

[0024] Aiming at the deficiencies of the prior art, the idea of ​​the present invention is to use the thrust as the direct control target and use the nonlinear model predictive control method for control, thereby improving the control accuracy and response speed of the aeroengine control system.

[0025] The technical scheme of the present invention is described in detail below in conjunction with accompanying drawing:

[0026] The nonlinear model predictive control system structure of the present invention is as follows figure 1 shown. The engine nonlinear model estimates unmeasurable parameters, and the NMP module includes an optimization submodule and a prediction model submodule. The present invention uses an online sliding window deep neural network (OL-SW-DNN) as a prediction model, which is better than traditional neural networks and support vector regression And other shallow network structures have strong fitting ability and improve the prediction accuracy. In the o...

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Abstract

The invention discloses an aero-engine direct thrust control method based on nonlinear model prediction control. Thrust directly serves as the control target in the method, and the situation that parameters which cannot be measured serve as the control target in a traditional control method is avoided. An online sliding window deep neural network serves as a prediction model, a deep learning structure is adopted in the model, the model precision can be improved, training data are selected online on the basis of a sliding window method, and sensibility for training data noise is lowered. Compared with current popular control methods, through the provided method, the acceleration time is shortened by 0.425 seconds, and the response speed is increased by about 1.14 times.

Description

technical field [0001] The invention relates to an aeroengine control method, in particular to an aeroengine direct thrust control method based on nonlinear model predictive control. Background technique [0002] The main function of an aeroengine is to provide thrust to an aircraft quickly and accurately. Traditional engine control strategies are sensor-based, that is, to indirectly control thrust by controlling engine measurable parameters such as rotor speed, engine pressure ratio (EPR) or other measurable parameters. However, due to factors such as degradation, manufacturing and manufacturing tolerances, the correspondence between engine thrust and measurable parameters will change. Therefore, if the traditional control idea is adopted, the engine thrust control error must exist. In addition, in order to ensure the safe and stable operation of the engine at the worst operating point, the traditional control strategy often maintains a large safety margin, which will gre...

Claims

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Application Information

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IPC IPC(8): F02C9/00F02C9/28
CPCF02C9/00F02C9/28F05D2260/81F05D2270/709
Inventor 郑前钢柳亚冰胡旭汪勇陈浩颖胡忠志张海波李秋红
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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