Variable cycle engine intelligent control method based on dynamic neural network

Abstract

The invention belongs to the technical field of aero-engine control and provides a variable cycle engine intelligent control method based on a dynamic neural network. A structure adjustment algorithmbased on a grey correlation analysis method is added into a neural network training algorithm, a neural network structure is adjusted, a dynamic neural network controller is constructed, and intelligent control of a variable-cycle engine is achieved. Dynamic neural network training is performed through a network structure adjustment algorithm based on the grey correlation analysis method designedin the invention, and a variable cycle engine dynamic intelligent controller based on the dynamic neural network is constructed. A coupling problem between nonlinear multivariables caused by increaseof control variables of the variable cycle engine and the problem that a traditional control method excessively depends on model precision are effectively solved. And meanwhile, the structure can be dynamically adjusted in a neural network training process so that the network structure is simpler, and an operation speed and the control precision are improved.

Description

technical field [0001] The invention belongs to the technical field of aero-engine control, and in particular relates to an intelligent control method of a variable-cycle engine based on a dynamic neural network. Background technique [0002] The control system is an important part of an aero-engine and a key factor affecting the performance of an aero-engine. The variable cycle engine combines the advantages of turbojet and turbofan engines, and adds geometrically adjustable components. By adaptively adjusting the geometric shape, position and other parameters of these components, the variable cycle engine can have high unit thrust in the aircraft maneuvering phases such as acceleration and climb; it can have low fuel consumption in the subsonic cruising phase. However, due to the increase of adjustable components, the number of control variables increases, and the structure of the controller is more complex; at the same time, there is a strong coupling relationship betwee...

AI Technical Summary

Problems solved by technology

Most of the existing control methods are based on accurate engine models, which will cause the accuracy of the model to directly affect the control effect of the controller, and cannot solve the coupling problem between nonlinear variables well.

Due to the nonlinear characteristics of the neural network itself, the controller based on the neural network can well solve the coupling problem between multiple variables, but during training, it is impossible to directly determine the structure of the hidden layer. If the structure is too large, the training will be too slow and Overfitting; the required accuracy cannot be achieved if the structure is too small

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