Hypersonic aerocraft neural network composite learning non-backstepping control method

A hypersonic and backstepping control technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems of poor practicability

Active Publication Date: 2017-12-15
NORTHWESTERN POLYTECHNICAL UNIV +1
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AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of poor practicability of existing hypersonic vehicle control methods

Method used

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Embodiment Construction

[0066] refer to figure 1 . The specific steps of the hypersonic vehicle neural network compound learning non-backstepping control method of the present invention are as follows:

[0067] (a) Establish a hypersonic vehicle longitudinal channel dynamics model:

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] The dynamic model is composed of five state variables X=[V, h, α, γ, q] T and two control inputs U=[δ e ,β] T Composition; where, V represents velocity, γ represents track inclination, h represents height, α represents angle of attack, q represents pitch angular velocity, δ e is the rudder deflection angle, β is the throttle valve opening; T, D, L and M yy Represent thrust, drag, lift and pitching torque respectively; m, I yy , μ and r represent the mass, the moment of inertia of the pitch axis, the gravitational coefficient and the distance from the center of the earth;

[0074] The relevant torque and parameters are defined as follows:

[0075] ...

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Abstract

The present invention discloses a hypersonic aerocraft neural network composite learning non-backstepping control method. The technical problem is solved that a current hypersonic aerocraft control method is bad in practicality. The technical scheme comprises: performing transformation of an attitude subsystem strict feedback form, obtaining an output feedback form, employing a high-gain observer to perform estimation of newly defined variables, and providing basis for subsequent design of a controller; allowing the controller to consider the lump nondeterminacy of the system, and only requiring one neural network to perform approximation, wherein the controller is simple in design and is convenient for engineering realization; aiming at control of unknown cases of a gain function, designing the controller based on the parameter linearization expression mode; and introducing system modeling errors, and constructing a neural network composite updating rule and a parameter adaptive composite updating rule to realize fast tracking of a hypersonic aerocraft. The effective estimation of unknown states is realized based on the high-gain observer, the repeat design of the virtual controlled quantity is not needed so as to simple the design of the controller. the realization is easy, and the practicality is good.

Description

technical field [0001] The invention relates to a hypersonic vehicle control method, in particular to a hypersonic vehicle neural network composite learning non-backstep control method. Background technique [0002] As a high-precision weapon with rapid strike capability, hypersonic aircraft has attracted great attention from many military powers. Due to the integrated design of the engine / body, coupled with the complex dynamic model and flight environment, the hypersonic vehicle has the characteristics of strong nonlinearity and strong uncertainty. These characteristics make the design of hypersonic vehicle controllers face great challenges. Therefore, the handling of uncertainty is crucial to the safe flight of hypersonic vehicles. [0003] As a typical control method, the backstepping method is widely used in the control of hypersonic vehicles. However, there are inherent defects in the traditional backstepping design. Using the backstepping method to design the contr...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 许斌程怡新郭雨岩张睿
Owner NORTHWESTERN POLYTECHNICAL UNIV
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