{"title":"无线最优充电龙门机器人系统的模糊自适应控制","authors":"Wen-Shyong Yu, Yufeng Lin","doi":"10.1109/ICMLC48188.2019.8949304","DOIUrl":null,"url":null,"abstract":"This paper mainly studies the realization of the wireless optimal charging gantry robot system using type-2 fuzzy adaptive control for mobile rechargeable devices. The wireless charging system is based on the energy management systems using the adaptive control algorithm to achieve the maximum charging power control. The type-2 fuzzy dynamic model is used to approximate the charging system in accordance with current standards without constructing sector dead-zone inverse, where the parameters of the fuzzy model are obtained both from the fuzzy inference and online update laws. The tracking trajectory tore chargeable devices including forward/inverse kinematics written by C# in Visual Studio is used for obtaining the joint angles of the xyz table corresponding to the desired trajectory. By feedback the charging current from the coil to detect position of the mobile devices, the optimal charging device tracking algorithm is given for obtaining the shortest distance and maximum power transmission between the induction coil and the rechargable device. Based on the Lyapunov criterion and Riccati-inequality, the control scheme is derived to stabilize the closed-loop system such that all states of the system are guaranteed to be bounded due to uncertainties, dead-zone nonlinearities, and external disturbances. The advantage of the proposed control scheme is that it can better handle the vagueness or uncertainties inherent in linguistic words using fuzzy set membership functions with adaptation capability by linear analytical results instead of estimating non-linear system functions as the system parameters are unknown. Finally, both simulation and experimental results are provided to verify the validity of the wireless optimal charging system.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fuzzy Adaptive Control for Wireless Optimal Charging Gantry Robot System\",\"authors\":\"Wen-Shyong Yu, Yufeng Lin\",\"doi\":\"10.1109/ICMLC48188.2019.8949304\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper mainly studies the realization of the wireless optimal charging gantry robot system using type-2 fuzzy adaptive control for mobile rechargeable devices. The wireless charging system is based on the energy management systems using the adaptive control algorithm to achieve the maximum charging power control. The type-2 fuzzy dynamic model is used to approximate the charging system in accordance with current standards without constructing sector dead-zone inverse, where the parameters of the fuzzy model are obtained both from the fuzzy inference and online update laws. The tracking trajectory tore chargeable devices including forward/inverse kinematics written by C# in Visual Studio is used for obtaining the joint angles of the xyz table corresponding to the desired trajectory. By feedback the charging current from the coil to detect position of the mobile devices, the optimal charging device tracking algorithm is given for obtaining the shortest distance and maximum power transmission between the induction coil and the rechargable device. Based on the Lyapunov criterion and Riccati-inequality, the control scheme is derived to stabilize the closed-loop system such that all states of the system are guaranteed to be bounded due to uncertainties, dead-zone nonlinearities, and external disturbances. The advantage of the proposed control scheme is that it can better handle the vagueness or uncertainties inherent in linguistic words using fuzzy set membership functions with adaptation capability by linear analytical results instead of estimating non-linear system functions as the system parameters are unknown. Finally, both simulation and experimental results are provided to verify the validity of the wireless optimal charging system.\",\"PeriodicalId\":221349,\"journal\":{\"name\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC48188.2019.8949304\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC48188.2019.8949304","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy Adaptive Control for Wireless Optimal Charging Gantry Robot System
This paper mainly studies the realization of the wireless optimal charging gantry robot system using type-2 fuzzy adaptive control for mobile rechargeable devices. The wireless charging system is based on the energy management systems using the adaptive control algorithm to achieve the maximum charging power control. The type-2 fuzzy dynamic model is used to approximate the charging system in accordance with current standards without constructing sector dead-zone inverse, where the parameters of the fuzzy model are obtained both from the fuzzy inference and online update laws. The tracking trajectory tore chargeable devices including forward/inverse kinematics written by C# in Visual Studio is used for obtaining the joint angles of the xyz table corresponding to the desired trajectory. By feedback the charging current from the coil to detect position of the mobile devices, the optimal charging device tracking algorithm is given for obtaining the shortest distance and maximum power transmission between the induction coil and the rechargable device. Based on the Lyapunov criterion and Riccati-inequality, the control scheme is derived to stabilize the closed-loop system such that all states of the system are guaranteed to be bounded due to uncertainties, dead-zone nonlinearities, and external disturbances. The advantage of the proposed control scheme is that it can better handle the vagueness or uncertainties inherent in linguistic words using fuzzy set membership functions with adaptation capability by linear analytical results instead of estimating non-linear system functions as the system parameters are unknown. Finally, both simulation and experimental results are provided to verify the validity of the wireless optimal charging system.