Hypersonic aerocraft neural network composite learning control method based on robustness design

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

Active Publication Date: 2017-12-15
NORTHWESTERN POLYTECHNICAL UNIV +1
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Problems solved by technology

[0005] In order to overcome the shortcomings of poor practicability of existing hypersonic vehicle control methods, the present invention provides a hypersonic vehicle neural network composite learning control method based on robust design

Method used

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  • Hypersonic aerocraft neural network composite learning control method based on robustness design

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Embodiment Construction

[0065] refer to figure 1 . The specific steps of the hypersonic vehicle neural network compound learning control method based on the robust design of the present invention are as follows:

[0066] (a) Establish a hypersonic vehicle longitudinal channel dynamics model:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] The longitudinal channel dynamics model consists 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;

[0073] The relevant torque and parameters are defin...

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Abstract

The present invention discloses a hypersonic aerocraft neural network composite learning control method based on robustness design. The objective of the invention is to solve the technical problem 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; and considering control of unknown cases of a gain function, introducing the upper and lower bound information, and designing a robustness item to ensure stability of the system. Because the strict feedback form is transformed to an output feedback form to effectively avoid approximation of virtual control amount required for future through adoption of the neural network; aiming at the system nondeterminacy, the robustness item is designed to ensure the stability of the system; and modeling errors are constructed to design a neural network composite learning updating rule so as to improve the neural network learning speed.

Description

technical field [0001] The invention relates to a hypersonic vehicle control method, in particular to a hypersonic vehicle neural network composite learning control method based on robust design. 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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 许斌程怡新郭雨岩张睿
Owner NORTHWESTERN POLYTECHNICAL UNIV
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