Fuzzy adaptive PID controller design method of aero-engine based on RBF neural network feedforward

An aero-engine, fuzzy self-adaptive technology, applied in the direction of self-adaptive control, general control system, control/regulation system, etc., can solve the problems of large variation of engine characteristics and complex aerodynamic thermodynamics

Inactive Publication Date: 2017-03-08
SHENYANG AEROSPACE XINGUANG GRP
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Problems solved by technology

[0009] The technical problem to be solved by the present invention is to consider that the aeroengine and its components have a wide operating range under the influence of external conditions and internal control, so that the characteristics of the engine in this operating range vary greatly, and the process is complicated. The working process of aerodynamic thermodynamics

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  • Fuzzy adaptive PID controller design method of aero-engine based on RBF neural network feedforward
  • Fuzzy adaptive PID controller design method of aero-engine based on RBF neural network feedforward
  • Fuzzy adaptive PID controller design method of aero-engine based on RBF neural network feedforward

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Embodiment Construction

[0048] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so as to define the protection scope of the present invention more clearly.

[0049] A kind of fuzzy PID self-adaptive controller design method of aero-engine based on RBF neural network feed-forward of the present invention comprises the following steps:

[0050] Step 1), select the component-level model as the nonlinear model of the aeroengine;

[0051] Step 2), aiming at the nonlinear model design in step 1) aeroengine fuzzy adaptive PID controller, using the model

[0052] Paste control law enables online tuning of controller parameters;

[0053] Step 3), based on the steady-state data of the full-state test run of the aero-engine, using the powerful nonlinear mapping capability of the RBF neural network

[0054] Force, train the feed-forward controller that can output accurate feed-forward amount;

[0055] Step 4), the RBF neural network...

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Abstract

The invention discloses a fuzzy PID adaptive controller design method of an aero-engine based on RBF neural network feedforward and belongs to the aero-engine numerical control system field. The method is used for improving acceleration and deceleration work states of the aero-engine and components under effects of an extraneous condition and internal control so that the engine possesses a good acceleration and deceleration characteristic. The technical scheme is characterized by based on an aero-engine steady state test data sample, using an RBF neural network to carry out off-line training so as to form a feedforward controller; and based on that, adding a fuzzy adaptive PID controller. In the invention, through carrying out acceleration and deceleration control on each state of the aero-engine, the controller possesses a good tracking effect and a good controller parameter online setting capability, control quality of the system in a large range can be further increased and a control difficulty brought by a non-linear characteristic of the engine can be effectively solved.

Description

technical field [0001] The invention discloses a fuzzy self-adaptive PID controller design method based on RBF neural network feedforward for an aero-engine, which belongs to the field of aero-engine numerical control systems and is used to improve the acceleration and deceleration of aero-engines and their components under the influence of external conditions and internal control The working state makes the engine have good acceleration and deceleration characteristics. Background technique [0002] Aeroengines and their components have a wide operating range under the influence of external conditions and internal controls. The external conditions mainly include: the total temperature and total pressure of the engine inlet determined by the flight M number and flight altitude. Its internal control functions mainly include: the amount of fuel in the main combustion chamber and the afterburner, the throat area of ​​the reaction nozzle, the installation angle of the blades (f...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 申春艳姜锐景月汤庚韩冬张景峰张春艳王惠
Owner SHENYANG AEROSPACE XINGUANG GRP
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