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Four-rotor formation fault-tolerant control method based on adaptive neural network

A neural network, fault-tolerant control technology, applied in the field of multi-agent systems

Active Publication Date: 2020-11-17
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Ordinary neural networks can obtain the weight matrix of the neural network through online or offline learning, but it is often difficult to analyze the stability of the system, while the adaptive neural network regards the weight matrix of the neural network as an adaptive variable, through The error information of the system updates the weight of the neural network. The adaptive neural network can not only realize the online self-learning of the neural network, but also ensure the stability of the system tracking error and the boundedness of the weight parameters of the neural network through the Lyapunov criterion. sex

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  • Four-rotor formation fault-tolerant control method based on adaptive neural network
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  • Four-rotor formation fault-tolerant control method based on adaptive neural network

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[0122] And considering that the first aircraft has a partial actuator failure during the formation process, and the second and third aircraft have no faults during the operation process, the time and magnitude of the actuator failure of the first aircraft are as follows:

[0123]

[0124] For verifying the fault-tolerant control effect of the present invention, use the Simulink platform of MATLAB to carry out simulation verification, simulation result is as follows:

[0125] image 3 It shows the tracking effect of the inner loop subsystem on the desired attitude. Because the expected attitude angle given by the outer loop changes, there is a certain tracking error. Depend on Figure three It can be seen that the tracking error is small and the convergence speed is fast. In the case of partial failure of the actuator, the stability of the system and better control performance can still be guaranteed.

[0126] From Figure 4 , 5, 6 It can be seen that even in the case of...

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Abstract

The invention discloses a quad-rotor formation fault-tolerant control method based on an adaptive neural network. Possible external interference in a formation control system comprising a plurality ofquad-rotor aircrafts is considered, a quad-rotor aircraft formation flight controller is designed through formation error information, a neural network is used to compensate the loss caused by a fault, and the Lyapunov stability theory is used to prove the stability, robustness and fault tolerance of the formation controller; on the basis of an adaptive neural network control method, an attitudefault-tolerant controller of a four-rotor inner ring is designed, the attitude fault-tolerant control effect is effectively improved, and it is proved by using the Lyapunov stability theory that the system can still track the expected attitude under the condition that an actuator fault occurs again.

Description

technical field [0001] The invention relates to a four-rotor formation fault-tolerant control method based on an adaptive neural network, and belongs to the technical field of multi-agent systems. Background technique [0002] Quadrotor aircraft is a rotor unmanned aerial vehicle that can take off and land vertically. The basic structure of quadrotor aircraft consists of a cross-shaped frame and four rotors mounted on the top of the frame. This special structure makes the control method of the quadrotor aircraft simpler and more convenient than other types of aircraft, and the attitude and position of the aircraft can be controlled by changing the rotation speed of the four rotors. The mutual coupling between the various control channels of the quadrotor aircraft is relatively weak and more flexible, so it has been widely used in power line inspection, SLAM, logistics transportation, aerial photography and other fields. [0003] With the rapid development of control theory ...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 张柯马人月姜斌陈谋盛守照甄子洋邵书义
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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