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Unmanned aerial vehicle cluster confrontation control method based on pigeon flock intelligent competitive learning

A technology of competitive learning and control method, which is applied in the field of unmanned aerial vehicle cluster confrontation control, to achieve the effect of improving solution speed and solution accuracy, improving efficiency and success rate, and strong real-time performance

Active Publication Date: 2021-05-11
BEIHANG UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a UAV swarm confrontation control method based on pigeon group intelligent competitive learning, which aims to solve the problem of motion control and combat target allocation of both sides of the UAV swarm in a complex confrontation environment, in line with the actual combat scene, improve On the basis of decision-making speed and system stability, further improve the autonomous operation level of UAV clusters

Method used

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  • Unmanned aerial vehicle cluster confrontation control method based on pigeon flock intelligent competitive learning
  • Unmanned aerial vehicle cluster confrontation control method based on pigeon flock intelligent competitive learning
  • Unmanned aerial vehicle cluster confrontation control method based on pigeon flock intelligent competitive learning

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Embodiment Construction

[0157] The effectiveness of the UAV swarm confrontation proposed by the present invention is verified below through specific examples. In this example, it is assumed that the red and blue drone clusters each have 30 drones, and one military base with high value. The combat objectives of both sides are to attack the enemy's military base and protect their own military base. The simulation environment of this example is intel i7-9750 processor, 2.60GHz main frequency, 8G memory, and the software is MATLAB 2019b version.

[0158] UAV swarm confrontation control method based on pigeon group intelligent competition learning, its realization process is as follows figure 2 As shown, the specific practical steps of this example are as follows:

[0159] Step 1: Initialize cluster confrontation environment settings

[0160] (1) Initialize the confrontation scene

[0161] The red military base is located at (0,0)m, and the initial position of the red drone cluster is randomly generat...

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Abstract

The invention discloses an unmanned aerial vehicle cluster confrontation control method based on pigeon flock intelligent competitive learning. The unmanned aerial vehicle cluster confrontation control method comprises the following implementation steps of 1, initializing cluster confrontation environment setting; 2, establishing an unmanned aerial vehicle cluster confrontation motion control model; 3, establishing a combat target distribution model based on a dynamic game theory; 4, establishing a cost function of unmanned aerial vehicle cluster combat target distribution; 5, designing a pigeon flock optimization algorithm based on a competitive learning mechanism; and 6, outputting an unmanned aerial vehicle cluster confrontation result. The method solves problems of motion control and combat target distribution in unmanned aerial vehicle cluster confrontation, is low in design cost, high in real-time performance and high in stability, has a practical application value, and improves an autonomous ability level in unmanned aerial vehicle cluster confrontation.

Description

technical field [0001] The invention relates to a UAV cluster confrontation control method based on pigeon group intelligent competitive learning, and belongs to the field of UAV autonomous control. Background technique [0002] Unmanned Aerial Vehicle (UAV) is an unmanned aircraft with the characteristics of "unmanned platform and manned system". Advantage. Due to the advantages of small size, low cost, strong battlefield survivability, and the ability to avoid casualties, UAVs are widely used in both civil and military fields. However, as the combat environment becomes increasingly complex, it is difficult for a single UAV to complete combat tasks due to the insufficient amount of information it can obtain and the load capacity of its assembled weapons. Through the establishment of a communication network, the UAV swarm can conduct real-time information interaction, realize coordinated investigation, search and strike, and improve the combat effectiveness of the UAV swar...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/10G06N3/00
CPCG05D1/107G06N3/006
Inventor 段海滨于月平邓亦敏霍梦真魏晨吴江周锐
Owner BEIHANG UNIV
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