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Unmanned aerial vehicle actuating mechanism fault diagnosis method based on extended Kalman filtering

A technology of extended Kalman and Kalman filter, which is applied in the field of fault diagnosis of UAV actuators based on extended Kalman filter, and can solve the problems of large number of filter observers and large amount of computation

Inactive Publication Date: 2020-11-13
BEIHANG UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

The traditional analytical model-based fault diagnosis method considers each fault type of each control actuator separately, and its limitation lies in the large number of filter observers and the large amount of computation.

Method used

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  • Unmanned aerial vehicle actuating mechanism fault diagnosis method based on extended Kalman filtering
  • Unmanned aerial vehicle actuating mechanism fault diagnosis method based on extended Kalman filtering
  • Unmanned aerial vehicle actuating mechanism fault diagnosis method based on extended Kalman filtering

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Embodiment 1

[0051] The first step: select the state most relevant to the UAV sensor measurement as the state vector of the fault diagnosis Kalman filter, and define the state vector as x=[p,q,r] T ; Among them, p represents the roll rate, q represents the pitch rate, and r represents the yaw rate.

[0052] The second step: take the left elevon and right elevon of the UAV as the research object of fault diagnosis, and define the control vector as u=[δ 1 ,δ 2 ] T , where δ 1 Indicates the right elevon control amount, δ 2 Represents the left elevon control quantity, and calculates the discrete matrix of the fault diagnosis Kalman filter:

[0053]

[0054]

[0055] Among them, F represents the state transition matrix of the fault diagnosis Kalman filter, eye (3) represents the third-order identity matrix, [I xx , I yy , I zz ] represents the moment of inertia of the UAV, b represents the width of the wing, and c A represents the mean geometric chord length, Q * Indicates the dy...

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Abstract

The invention discloses an unmanned aerial vehicle actuating mechanism fault diagnosis method based on extended Kalman filtering. The extended Kalman filtering technology is adopted to introduce the state of an actuating mechanism into a state vector of a filter, and only one filter can be used for monitoring all health states of one actuating mechanism, so that the method has the advantages of small calculated amount and high fault diagnosis speed. According to the unmanned aerial vehicle actuating mechanism fault diagnosis method based on extended Kalman filtering, three fault types of jamming, swinging and proportionality coefficient of the actuating mechanism can be comprehensively monitored, and the unmanned aerial vehicle actuating mechanism fault diagnosis method can be suitable fora flight control system which is limited in operational capability and requires rapid diagnosis and determination of the fault type of the unmanned aerial vehicle actuating mechanism.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of unmanned aerial vehicles, in particular to a method for fault diagnosis of unmanned aerial vehicle actuators based on extended Kalman filtering. Background technique [0002] In the flight control system, the main purpose of fault diagnosis and system reconfiguration is to improve the safety and reliability of the aircraft. For any failure situation, the flight control system is required to have the ability of fast and accurate isolation, safe and stable reconfiguration. Therefore, the judgment and reconstruction of self-adaptation, self-organization and self-decision of the fault system are the key technologies to be solved in the fault-tolerant flight control system. [0003] In recent years, in the field of UAV fault diagnosis research, fault diagnosis methods can be roughly classified into the following three categories: methods based on analytical models, methods based on knowledg...

Claims

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Application Information

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IPC IPC(8): G05B23/02B64F5/60
CPCB64F5/60G05B23/0254
Inventor 张谦徐一钒张京娟王学运于泽龙
Owner BEIHANG UNIV
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