Particle swarm optimization method for the control of a fleet of Unmanned Aerial Vehicles

12th European Workshop on Advanced Control and Diagnosis

Belkadi, A.^1,2 ; Ciarletta, L.^3,4 ; Theilliol, D.^1,2

Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine, CNRS, 2 avenue de la forêt de Haye, Vandoeuvre-les-Nancy

F-54516, France^1

CNRS, CRAN, UMR 7039, France^2

Permanent Member the Madynes Research Team of Loria, UMR 7503, France^3

École des Mines Nancy, Université de Lorraine, France^4

关键词: Fleet control;    generating path;    Objective functions;    Optimization method;    Particle swarm optimization method;    Particle swarm optimization method (PSO);    PSO algorithms;    Virtual leader;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012015/pdf
DOI  :  10.1088/1742-6596/659/1/012015

来源: IOP

【 摘 要 】

This paper concerns a control approach of a fleet of Unmanned Aerial Vehicles (UAV) based on virtual leader. Among others, optimization methods are used to develop the virtual leader control approach, particularly the particle swarm optimization method (PSO). The goal is to find optimal positions at each instant of each UAV to guarantee the best performance of a given task by minimizing a predefined objective function. The UAVs are able to organize themselves on a 2D plane in a predefined architecture, following a mission led by a virtual leader and simultaneously avoiding collisions between various vehicles of the group. The global proposed method is independent from the model or the control of a particular UAV. The method is tested in simulation on a group of UAVs whose model is treated as a double integrator. Test results for the different cases are presented.