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Iterative learning correction-based aero-engine intelligent rotation peed control method

An aero-engine, iterative learning technology, which is applied to engine components, fuel control of turbine/propulsion devices, and turbine/propulsion fuel delivery systems, etc. Overshoot and other issues, to ensure stability and generalization ability, improve the effect of control accuracy

Active Publication Date: 2019-01-08
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

Firstly, the response of the control system may have a large overshoot, and secondly, the online correction of the parameters of the controller requires a large amount of calculation, which makes it difficult to apply the controller to the real aeroengine speed control

Method used

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  • Iterative learning correction-based aero-engine intelligent rotation peed control method

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Embodiment Construction

[0040] The specific embodiments of the present invention will be further described below in conjunction with the drawings.

[0041] The intelligent speed control method of aeroengine based on iterative learning correction described in the present invention is characterized in that it comprises the following steps:

[0042] Step A) According to the relationship between the main fuel quantity of the engine model and the high-pressure rotor speed, the neural network is used to construct the NARMA-L2 model of the aero engine speed control system;

[0043] Step A1), select the main fuel quantity of the engine as the input quantity u[k], and the high-pressure rotor speed as the output quantity y[k], and construct the NARMA-L2 model according to the selected input and output quantity as:

[0044] y[k]=f 0 (y[k-1],y[k-2],…,y[k-n],u[k-1],u[k-2],…,u[k-n])+g 0 (y[k-1],y[k-2],…,y[k-n],u[k-1],u[k-2],…,u[k-n])u[k]

[0045] Among them, k is the time scale of the system, f 0 And g 0 For network mapping...

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Abstract

The invention discloses an iterative learning correction-based aero-engine intelligent rotation speed control method. The method comprises the following steps of: establishing an NARMA-L2 model of a rotation speed control system by adoption of a neural network; and designing an iterative learning algorithm-based online rotation speed correction model by combining the NARMA-L2 model, so as to obtain an aero-engine rotation speed controller with self-regulation ability in a certain working condition range in envelopes. The method is capable of solving the problems that difficulties exist in traditional neural network-based prediction model establishment and online control rate solution and control quality of controllers expanded and applied in envelopes is reduced, is suitable for controlling rotation speeds of engines at different working points in certain flight envelopes, and has positive promotion effect for eliminating steady-state errors of engine control systems and improving engine rotation speed control quality.

Description

Technical field [0001] The invention belongs to the technical field of aeroengine speed control, and in particular relates to an aeroengine intelligent speed control method based on iterative learning algorithm correction. Background technique [0002] The aeroengine is a complex thermodynamic system with strong uncertainty and time-variability, which makes the design of the controller difficult. Neural networks are widely used due to their good approximation and generalization capabilities. The nonlinear auto regressive moving average with feedback linearization (NARMA-L2) controller is an effective artificial neural network controller architecture. Under certain conditions, the input-output relationship of the nonlinear system can be identified by the NARMA-L2 model, and the control rate can be obtained through simple mathematical transformations. This model was first introduced by Narendra and Mukhopadhyay, and has now been widely used in many nonlinear systems. [0003] Howe...

Claims

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Application Information

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IPC IPC(8): F02C9/28
Inventor 鲁峰闫召洪黄金泉仇小杰
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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