Mukul Gaur, Himanshu Chaudhary, S. Khatoon, Ravindra Singh
{"title":"Genetic algorithm based trajectory stabilization of quadrotor","authors":"Mukul Gaur, Himanshu Chaudhary, S. Khatoon, Ravindra Singh","doi":"10.1109/CIPECH.2016.7918731","DOIUrl":null,"url":null,"abstract":"This paper deals with control and stabilization of a quadcopter UAV using Genetic algorithm tuned PID controller. In this work, conventional GA is improved in two ways: firstly, crossover fraction and mutation rate is made adaptive by using a Fussy logic controller. Secondly, an advanced randomness is provided in GA by changing half of its initial population with random candidates after a fixed generations. Simulation results proven to be more optimised with the proposed controller in respect to the doth transient response and robustness in presence of adverse condition or disturbances.","PeriodicalId":247543,"journal":{"name":"2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Second International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIPECH.2016.7918731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper deals with control and stabilization of a quadcopter UAV using Genetic algorithm tuned PID controller. In this work, conventional GA is improved in two ways: firstly, crossover fraction and mutation rate is made adaptive by using a Fussy logic controller. Secondly, an advanced randomness is provided in GA by changing half of its initial population with random candidates after a fixed generations. Simulation results proven to be more optimised with the proposed controller in respect to the doth transient response and robustness in presence of adverse condition or disturbances.