Jullian Dominic D. Ducut, Alezander Mikhail O. Galindo, R. Billones, E. Dadios, I. Valenzuela
{"title":"基于神经模糊的无人机安全著陆控制系统","authors":"Jullian Dominic D. Ducut, Alezander Mikhail O. Galindo, R. Billones, E. Dadios, I. Valenzuela","doi":"10.1109/HNICEM54116.2021.9732007","DOIUrl":null,"url":null,"abstract":"The number of aerial drone users continue to increase due to its availability, usage, and depreciation. The low cost of drones results in low-quality components that are prone to damage. One of the most common problems of drones is the landing system, where most drones crash due to uncontrolled maneuvering of the drone. In this study, Adaptive Neuro-Fuzzy inference Systems (ANFIS) using MATLAB was developed to perform a safe landing system on low-cost drones where the Gaussian Bell Membership function was used due to a low training error of 0.0015693.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neuro-Fuzzy based Safe Landing Control System for UAVs\",\"authors\":\"Jullian Dominic D. Ducut, Alezander Mikhail O. Galindo, R. Billones, E. Dadios, I. Valenzuela\",\"doi\":\"10.1109/HNICEM54116.2021.9732007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The number of aerial drone users continue to increase due to its availability, usage, and depreciation. The low cost of drones results in low-quality components that are prone to damage. One of the most common problems of drones is the landing system, where most drones crash due to uncontrolled maneuvering of the drone. In this study, Adaptive Neuro-Fuzzy inference Systems (ANFIS) using MATLAB was developed to perform a safe landing system on low-cost drones where the Gaussian Bell Membership function was used due to a low training error of 0.0015693.\",\"PeriodicalId\":129868,\"journal\":{\"name\":\"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HNICEM54116.2021.9732007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HNICEM54116.2021.9732007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neuro-Fuzzy based Safe Landing Control System for UAVs
The number of aerial drone users continue to increase due to its availability, usage, and depreciation. The low cost of drones results in low-quality components that are prone to damage. One of the most common problems of drones is the landing system, where most drones crash due to uncontrolled maneuvering of the drone. In this study, Adaptive Neuro-Fuzzy inference Systems (ANFIS) using MATLAB was developed to perform a safe landing system on low-cost drones where the Gaussian Bell Membership function was used due to a low training error of 0.0015693.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
