Unmanned air vehicle attitude robust fault tolerance control method based on neural network observer

An unmanned aerial vehicle and neural network technology, applied in the field of aircraft attitude fault-tolerant control, can solve problems such as cumulative parameter estimation, divergence, and inaccuracy

Active Publication Date: 2014-09-17
南京晓飞智能科技有限公司
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Problems solved by technology

However, nonlinear predictive control is still immature in system stability analysis.
Inversion control is a controller design method based on Lyapunov stability theory. It has been widely concerned by researchers since it was proposed in the 1990s. However, it also has three main defects. (1) Differential expansion problem ; (2) requires a strict fe

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  • Unmanned air vehicle attitude robust fault tolerance control method based on neural network observer

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Embodiment Construction

[0061] Below in conjunction with accompanying drawing and embodiment the invention is described in detail:

[0062] The fault-tolerant control method proposed in this application only needs to design a robust auxiliary system to achieve robust fault-tolerant control, while the design of the reconfigurable controller is based on the dynamic model of the auxiliary system. Therefore, compared with the traditional method, this paper is more concise and convenient in the design steps, and avoids the problem of how to design an FDI unit with low false positives and false positives. So as to bypass this problem and realize the robust fault-tolerant control of the flight control system.

[0063] The present invention provides as figure 1 The shown robust fault-tolerant control method for the attitude of unmanned aerial vehicle based on the neural network observer, the specific steps are as follows, and it is characterized in that:

[0064] 1) Put the variable signal Input K to th...

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Abstract

Based on a neural network technology and an instruction filtering inversion method, the invention discloses a robust fault tolerance control system design structure based on instruction filtering inversion. Firstly, a mathematical model of an NSV attitude control system is given out, uncertainties and external disturbance caused by modeling errors are considered based on the mathematical model, a state equation of the NSV attitude control system under the fault of a control surface is also considered. The method mainly includes the two design parts including design of an auxiliary system and design of a controller based on the auxiliary system. The auxiliary system is introduced into the neural network to ensure robustness of the auxiliary system, and stability of a closed-loop system is strictly proved through Lyapunov theorem. Meanwhile, simulation is carried out on an attitude control system of a corresponding air vehicle, and a result shows that the method enables the uncertain flight control system with the external disturbance to have the ideal fault tolerance tracking performance under damage to the control surface.

Description

technical field [0001] The invention relates to the field of aircraft attitude fault-tolerant control, in particular to an unmanned aerial vehicle attitude robust fault-tolerant control method based on a neural network observer. [0002] Background technique [0003] Near space (Near Space) refers to the space area 20km~100km away from the sea level, and current human exploration activities rarely involve this height range. It has the atmospheric stratosphere region (height 20km~55km), the atmospheric mesosphere region (height 55km~85km) and a small part of the warming layer region (height above 85km), of which the region below 60km is the non-ionosphere, and the region above 60km is the The ionosphere, the atmospheric composition in most of its space is a homogeneous atmosphere (the area below about 90km in height). It is precisely because of its unique spatial location that the near space has a unique flight environment and nature, so it has great strategic significance ...

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Application Information

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IPC IPC(8): G05D1/08G05B13/04
Inventor 周洪成胡艳
Owner 南京晓飞智能科技有限公司
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