Complex learning based non-minimum phase aircraft neural network control method

A neural network control, non-minimum phase technology, applied in the field of aircraft control

Active Publication Date: 2018-11-06
NORTHWESTERN POLYTECHNICAL UNIV
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  • Description
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AI Technical Summary

Problems solved by technology

[0004] Aiming at the non-minimum phase aircraft control problem, the present invention designs a non-minimum phase aircraft neural network control method based on composite learning

Method used

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  • Complex learning based non-minimum phase aircraft neural network control method
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  • Complex learning based non-minimum phase aircraft neural network control method

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Embodiment

[0123] refer to figure 1 , the present invention is based on compound learning non-minimum phase aircraft neural network control method applied to hypersonic aircraft, through the following steps to achieve:

[0124] (a) Establish a non-minimum phase hypersonic vehicle longitudinal channel dynamics model:

[0125]

[0126] Among them, V represents velocity, γ represents track inclination, h represents height, α represents angle of attack, q represents pitch angle velocity, δ e Indicates rudder deflection angle, Φ indicates throttle valve opening; T, D, L and M yy represent thrust, drag, lift and pitching moment respectively; m, I yy and g denote the mass, the moment of inertia of the pitch axis, and the acceleration due to gravity, respectively.

[0127] The relevant forces, moments and parameters are defined as follows:

[0128]

[0129]

[0130] in represents the dynamic pressure, ρ represents the air density, represents the mean aerodynamic chord length, z ...

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Abstract

The invention relates to a complex learning based non-minimum phase aircraft neural network control method, a longitudinal channel model of an aircraft is firstly decomposed into a speed subsystem anda height subsystem, PID is used to control the speed subsystem, and a back-stepping method is used to control the height subsystem. As for the height subsystem, the unstable inner dynamic state is changed to the asymptotic stable inner dynamic state on the basis of outputting redefinition, the inner dynamic state of the system outputting redefinition is subjected to coordinate transformation, anda pitch angel instruction used for designing a controller is derived; the unknown nonlinear kinetics of the system is estimated through the neural network, a model error signal is designed, a complexlearning algorithm is given in combination with a tracking error, and the approximation performance of the nonlinear kinetics of the neural network is improved. The inner dynamic state is stabilizedby outputting redefinition, the uncertain kinetics of the aircraft is estimated on the basis of the complex learning neural network, and a novel concept is provided for controlling the non-minimum phase aircraft.

Description

technical field [0001] The invention relates to an aircraft control method, in particular to a compound learning-based non-minimum phase aircraft neural network control method, which belongs to the field of aircraft control. Background technique [0002] Facing the new requirements for aircraft technology in the military and civilian fields, the flight envelope of modern aircraft continues to expand. The innovative configuration design of aircraft and the complex flight environment lead to complex nonlinear and strong uncertainty in aircraft dynamics. There is a coupling between the aircraft elevator and the aerodynamic force, which causes the aircraft to exhibit non-minimum phase characteristics. The non-minimum phase characteristics make the dynamic inverse design unable to be directly applied. At present, the influence of the elevator on the lift is mostly compensated by adding canard control surfaces, so that the system becomes a minimum phase system, but the addition of...

Claims

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Application Information

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IPC IPC(8): G05B13/04G05D1/10
CPCG05B13/027G05B13/042G05D1/101
Inventor 许斌王霞杨林肖勇张君蔡华
Owner NORTHWESTERN POLYTECHNICAL UNIV
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