Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

View through CrossRef
Recently, distributed coordination control of the unmanned aerial vehicle (UAV) swarms has been a particularly active topic in intelligent system field. In this paper, through understanding the emergent mechanism of the complex system, further research on the flocking and the dynamic characteristic of UAV swarms will be given. Firstly, this paper analyzes the current researches and existent problems of UAV swarms. Afterwards, by the theory of stochastic process and supplemented variables, a differential-integral model is established, converting the system model into Volterra integral equation. The existence and uniqueness of the solution of the system are discussed. Then the flocking control law is given based on artificial potential with system consensus. At last, we analyze the stability of the proposed flocking control algorithm based on the Lyapunov approach and prove that the system in a limited time can converge to the consensus direction of the velocity. Simulation results are provided to verify the conclusion.
Title: Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms
Description:
Recently, distributed coordination control of the unmanned aerial vehicle (UAV) swarms has been a particularly active topic in intelligent system field.
In this paper, through understanding the emergent mechanism of the complex system, further research on the flocking and the dynamic characteristic of UAV swarms will be given.
Firstly, this paper analyzes the current researches and existent problems of UAV swarms.
Afterwards, by the theory of stochastic process and supplemented variables, a differential-integral model is established, converting the system model into Volterra integral equation.
The existence and uniqueness of the solution of the system are discussed.
Then the flocking control law is given based on artificial potential with system consensus.
At last, we analyze the stability of the proposed flocking control algorithm based on the Lyapunov approach and prove that the system in a limited time can converge to the consensus direction of the velocity.
Simulation results are provided to verify the conclusion.

Related Results

Magnetic Microrobotic Swarms in Fluid Suspensions
Magnetic Microrobotic Swarms in Fluid Suspensions
Abstract Purpose of Review Microrobotic swarms have attracted extensive attentions due to their potential in medical and bioengineering applications...
Tethered UAV-active defense against intelligent cluster
Tethered UAV-active defense against intelligent cluster
Purpose With the development of wireless networks and artificial intelligence technology, unmanned aerial vehicle (UAV) clusters are widely used in various fields...
Mixed-reality for unmanned aerial vehicle operations in near earth environments
Mixed-reality for unmanned aerial vehicle operations in near earth environments
Future applications will bring unmanned aerial vehicles (UAVs) to near Earth environments such as urban areas, causing a change in the way UAVs are currently operated. Of concern i...
Quantitative Analysis of Shallow Earthquake Sequences and Regional Earthquake Behavior: Implications for Earthquake Forecasting
Quantitative Analysis of Shallow Earthquake Sequences and Regional Earthquake Behavior: Implications for Earthquake Forecasting
<p>This study is a quantitative investigation and characterization of earthquake sequences in the Central Volcanic Region (CVR) of New Zealand, and several regions in New Zea...
Quantitative Analysis of Shallow Earthquake Sequences and Regional Earthquake Behavior: Implications for Earthquake Forecasting
Quantitative Analysis of Shallow Earthquake Sequences and Regional Earthquake Behavior: Implications for Earthquake Forecasting
<p>This study is a quantitative investigation and characterization of earthquake sequences in the Central Volcanic Region (CVR) of New Zealand, and several regions in New Zea...
Quantifying corn emergence using UAV imagery and machine learning
Quantifying corn emergence using UAV imagery and machine learning
Corn (Zea mays L.) is one of the important crops in the United States for animal feed, ethanol production, and human consumption. To maximize the final corn yield, one of the criti...
ACOUSTIC FIELD CHARACTERISTICS UAV SCREW
ACOUSTIC FIELD CHARACTERISTICS UAV SCREW
Unmanned aerial vehicles (UAVs) began to be actively used in civil and military spheres. During flight, UAV nodes emit noise into the environment, while the main radiation node is ...
UAV Radar imaging for cultural heritage: a first prototype
UAV Radar imaging for cultural heritage: a first prototype
&lt;p&gt;Nowadays, the use of Unmanned Aircraft Vehicle (UAV) based sensing technologies is widely considered in most disparate fields, including archaeology and cultural h...

Back to Top