{"title":"Metropolis criterion pigeon-inspired optimization for multi-UAV swarm controller","authors":"Jinghua Guan, Hongfei Cheng","doi":"10.20517/ir.2024.04","DOIUrl":null,"url":null,"abstract":"This paper presents a new multiple unmanned aerial vehicle swarm controller based on Metropolis criterion. This paper presents the design of a controller, utilizing the improved Metropolis criterion pigeon-inspired optimization (IMCPIO) and proportional-integrational-derivative (PID) algorithms, and conducts comparative experiments. Simulation outcomes demonstrate the enhanced performance of the multi-unmanned aerial vehicle formation controller, which is based on IMCPIO, when compared to the basic pigeon-inspired optimization (PIO) algorithm and the genetic algorithm. The IMCPIO algorithm for the energy difference discrimination makes it a faster convergence and more stable effective optimization. Hence, the controller introduced in this study proves to be both practical and resilient.","PeriodicalId":426514,"journal":{"name":"Intelligence & Robotics","volume":"33 31","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligence & Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20517/ir.2024.04","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a new multiple unmanned aerial vehicle swarm controller based on Metropolis criterion. This paper presents the design of a controller, utilizing the improved Metropolis criterion pigeon-inspired optimization (IMCPIO) and proportional-integrational-derivative (PID) algorithms, and conducts comparative experiments. Simulation outcomes demonstrate the enhanced performance of the multi-unmanned aerial vehicle formation controller, which is based on IMCPIO, when compared to the basic pigeon-inspired optimization (PIO) algorithm and the genetic algorithm. The IMCPIO algorithm for the energy difference discrimination makes it a faster convergence and more stable effective optimization. Hence, the controller introduced in this study proves to be both practical and resilient.