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Four-rotor-wing unmanned aerial vehicle formation controlling method based on self-adaption RBF neural network

A technology of quadrotor UAV and neural network, applied in the field of formation control of quadrotor UAV system, can solve problems that have not been fully studied

Active Publication Date: 2019-04-26
ZHEJIANG UNIV OF TECH
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Problems solved by technology

To the best of the authors' knowledge, and to the best of our knowledge, the problem of time-delays in 3D formation control of nonlinear multi-agent systems is understudied and remains a challenging task

Method used

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  • Four-rotor-wing unmanned aerial vehicle formation controlling method based on self-adaption RBF neural network
  • Four-rotor-wing unmanned aerial vehicle formation controlling method based on self-adaption RBF neural network
  • Four-rotor-wing unmanned aerial vehicle formation controlling method based on self-adaption RBF neural network

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Embodiment Construction

[0087] The implementation of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0088] A quadrotor UAV formation control method based on adaptive RBF neural network. In this experiment, six quadrotor UAVs are controlled in formation to form a figure 1 The regular hexahedron shown.

[0089] First, define the state equation of the quadrotor UAV system as follows:

[0090]

[0091] in

[0092]

[0093]

[0094]

[0095] f i1 (x i (t), v i (t)) = β i1 x i1 cos(v i2 ), f i2 (x i (t), v i (t)) = β i2 x i2 sin(v i1 ), f i3 (x i (t), v i (t)) = β i3 x i2 sin(v i3 ). and g i (·) is unknown in the actual formation process, and the values ​​of parameters α and β are shown in Table 1 and Table 2. The communication topology between 6 quadrotor UAVs is as follows: figure 2 shown. The position coordinates of each UAV in the formation and the time lag in the UAV state equation are as follows: ...

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Abstract

The invention discloses a four-rotor-wing unmanned aerial vehicle formation controlling method based on a self-adaption RBF neural network. A three-dimensional formation controlling scheme is studiedunder the condition that there exists dynamic uncertainty in a non-linear multi-four-rotor-wing unmanned aerial vehicle system with time-lag based on a radial basis function (RBF). a linear reduced order observer is designed for obtaining absolute and local state errors of each unmanned aerial vehicle, a Lyapunov function which can simply the designing of the controller is constructed to offset the time-lag existing in an unmanned aerial vehicle dynamic model, and the self-adaption RBF neural network is adopted for treating the non-linear dynamic uncertainty and unavoidable interference.

Description

technical field [0001] The invention relates to a four-rotor unmanned aerial vehicle formation control method based on an adaptive RBF neural network, and performs formation control of the four-rotor unmanned aerial vehicle system. Background technique [0002] At present, with the continuous development of aviation and avionics technology, drones play a huge role in both civilian fields such as rescue missions, forest fires, and express transportation, as well as military fields such as military reconnaissance and ground strikes. Still, there is a limit to what a single drone can accomplish. For example, a single UAV cannot completely obtain the overall environmental information of the target area environment due to factors such as the number of its own sensors or the limitation of the sensor angle. Therefore, the study of multi-UAV swarm formation has attracted great attention. Among them, the multi-agent formation research combined with machine learning is widely used i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/10G06N3/08
CPCG05D1/104G06N3/084
Inventor 张铭扬禹鑫燚丁沛炫欧林林
Owner ZHEJIANG UNIV OF TECH
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