{"title":"Ultrastick-25e无人机的自适应多变量控制器","authors":"E. N. Mobarez, Amr Sarhan Mahmoud, M. Ashry","doi":"10.1109/ICEENG45378.2020.9171730","DOIUrl":null,"url":null,"abstract":"Two types of adaptive control are designed for Ultrastick-25e UAV. This is to improve the control response to the Ultrastick-25e. The 1st adaptive autopilot method proposed is self-tuned PID using fuzzy. That is to retune the PID parameter to keep the UAV stable at all operating points. The 2nd adaptive autopilot method proposed is ANFIS controller. It is intelligent control technique. Testing against turbulence of the air, sensor’s noise effect and against the possibility of the model not completing perfectly (uncertainty) are a basic elements for making a comparison between the proposed controllers and evaluate their robustness. Through this paper Adaptive self-tuned PID using fuzzy and ANFIS controllers are used for the first time on Ultrastick-25e UAV. The comparisons between the proposed control systems during several scenarios confirm the robustness of ANFIS controller over the adaptive self-tuned PID using fuzzy.","PeriodicalId":346636,"journal":{"name":"2020 12th International Conference on Electrical Engineering (ICEENG)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Multi-variable Controllers for Ultrastick-25e UAV\",\"authors\":\"E. N. Mobarez, Amr Sarhan Mahmoud, M. Ashry\",\"doi\":\"10.1109/ICEENG45378.2020.9171730\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Two types of adaptive control are designed for Ultrastick-25e UAV. This is to improve the control response to the Ultrastick-25e. The 1st adaptive autopilot method proposed is self-tuned PID using fuzzy. That is to retune the PID parameter to keep the UAV stable at all operating points. The 2nd adaptive autopilot method proposed is ANFIS controller. It is intelligent control technique. Testing against turbulence of the air, sensor’s noise effect and against the possibility of the model not completing perfectly (uncertainty) are a basic elements for making a comparison between the proposed controllers and evaluate their robustness. Through this paper Adaptive self-tuned PID using fuzzy and ANFIS controllers are used for the first time on Ultrastick-25e UAV. The comparisons between the proposed control systems during several scenarios confirm the robustness of ANFIS controller over the adaptive self-tuned PID using fuzzy.\",\"PeriodicalId\":346636,\"journal\":{\"name\":\"2020 12th International Conference on Electrical Engineering (ICEENG)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 12th International Conference on Electrical Engineering (ICEENG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEENG45378.2020.9171730\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 12th International Conference on Electrical Engineering (ICEENG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEENG45378.2020.9171730","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Multi-variable Controllers for Ultrastick-25e UAV
Two types of adaptive control are designed for Ultrastick-25e UAV. This is to improve the control response to the Ultrastick-25e. The 1st adaptive autopilot method proposed is self-tuned PID using fuzzy. That is to retune the PID parameter to keep the UAV stable at all operating points. The 2nd adaptive autopilot method proposed is ANFIS controller. It is intelligent control technique. Testing against turbulence of the air, sensor’s noise effect and against the possibility of the model not completing perfectly (uncertainty) are a basic elements for making a comparison between the proposed controllers and evaluate their robustness. Through this paper Adaptive self-tuned PID using fuzzy and ANFIS controllers are used for the first time on Ultrastick-25e UAV. The comparisons between the proposed control systems during several scenarios confirm the robustness of ANFIS controller over the adaptive self-tuned PID using fuzzy.