Hypersonic aircraft neural anti-interference control method based on mixed event triggering mechanism

An event-triggered, hypersonic technology that can be used in adaptive control, general control systems, control/regulation systems, etc., to solve problems such as control errors and lack of limited airborne computing resources

Active Publication Date: 2020-08-04
ZHONGBEI UNIV
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AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem that the existing AHV neural control strategy is difficult to meet the real-time calculation of high-ultra-fast maneuvering flight, which leads to significant control errors and the existing

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  • Hypersonic aircraft neural anti-interference control method based on mixed event triggering mechanism
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  • Hypersonic aircraft neural anti-interference control method based on mixed event triggering mechanism

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Embodiment Construction

[0040] The present invention will be further described below in conjunction with specific examples.

[0041] A hypersonic vehicle neural anti-jamming control method based on a hybrid event trigger mechanism, the process is as follows figure 1 shown, including the following steps:

[0042] (1) Establish a control description-oriented AHV parameterized strict feedback kinematics / dynamics model:

[0043]

[0044] Among them, V is the speed, h is the height, α is the angle of attack, that is, AOA, γ is the track angle FPA, Q is the pitch angle rate PR, f i , g i (i=V, α, γ, Q) represent concentrated disturbance and AHV nominal dynamics, respectively;

[0045]

[0046]

[0047]

[0048]

[0049] In the formula, Δ represents the disturbance of the aerodynamic coefficient; g=32 represents the gravitational constant; m=300 and I yy =86722.54 are the mass and moment of inertia of the aircraft respectively; p=0.5ρ a V 2 is the dynamic pressure, ρ a = ρ a0 exp(-(h...

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Abstract

The invention discloses a hypersonic aircraft neural anti-interference control method based on a mixed event triggering mechanism, and relates to the technical field of aircraft automatic control. Themethod comprises the following steps: firstly, establishing a control description-oriented AHV parameterized kinematics/dynamics model; secondly, designing a minimum parameter learning neural networklearner with low computational complexity to eliminate the influence of various disturbances on the control system in real time; thirdly, constructing a mixed event triggering mechanism based on a relative triggering threshold value and an absolute triggering threshold value, effectively reducing the information communication traffic of the control loop on the premise of not sacrificing the tracking performance, and avoiding the defects that the triggering frequency is too frequent under a fixed triggering event and the control buffeting is caused by a larger control signal under the relativetriggering event; finally, integrating the MLP observer and the event trigger controller and realizing accurate tracking control over a given height/speed instruction. The method mainly solves the problem of AHV robust flight control under the influence of airborne computing resources and multi-source interference.

Description

technical field [0001] The invention relates to the technical field of aircraft automatic control, specifically a hypersonic aircraft neural anti-jamming control method based on a mixed event trigger mechanism, which mainly solves the problem of AHV robust flight control under the influence of onboard computing resources and multi-source interference. Background technique [0002] An air-breathing hypersonic vehicle (Airbreathing Hypersonic Vehicle, AHV) generally refers to an aircraft whose flight Mach number is not less than 5 Ma and which is powered by an air-breathing scramjet engine. AHV has important civil and military value and strategic significance, and has many advantages such as high speed, high maneuverability, and large flight envelope. In recent years, researchers at home and abroad have achieved fruitful research results in the design of AHV stable flight control by applying theories such as neural network, sliding mode control, adaptive control, and robust co...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042Y02T90/00
Inventor 邵星灵石燚张文栋刘俊
Owner ZHONGBEI UNIV
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