{"title":"sigma点光滑变结构滤波器在机械臂中的应用","authors":"M. Al-Shabi","doi":"10.1109/ICMSAO.2017.7934865","DOIUrl":null,"url":null,"abstract":"In this work, well known Sigma Point Kalman Filters (SPKFs); namely Unscented Kalman filter (UKF), the Cubature Kalman filter (CKF), and the Central Differences Kalman filter (CDKF) will be combined to the Smooth Variable Structure Filter (SVSF), in order to create stable and robust algorithms that can be applied to highly non-linear systems. The proposed algorithms will be applied into 4-DOF robotic arm that consists of one prismatic joint and three revolute joints (PRRR). By doing so, the stability of the proposed filters will be guaranteed. Which makes the comparison between the SPKFs easier. These methods will be executed using simulation experiments, and then they will be compared in terms of stability, robustness and the quality of the optimality.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Sigma-point Smooth Variable Structure Filters applications into robotic arm\",\"authors\":\"M. Al-Shabi\",\"doi\":\"10.1109/ICMSAO.2017.7934865\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, well known Sigma Point Kalman Filters (SPKFs); namely Unscented Kalman filter (UKF), the Cubature Kalman filter (CKF), and the Central Differences Kalman filter (CDKF) will be combined to the Smooth Variable Structure Filter (SVSF), in order to create stable and robust algorithms that can be applied to highly non-linear systems. The proposed algorithms will be applied into 4-DOF robotic arm that consists of one prismatic joint and three revolute joints (PRRR). By doing so, the stability of the proposed filters will be guaranteed. Which makes the comparison between the SPKFs easier. These methods will be executed using simulation experiments, and then they will be compared in terms of stability, robustness and the quality of the optimality.\",\"PeriodicalId\":265345,\"journal\":{\"name\":\"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMSAO.2017.7934865\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2017.7934865","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sigma-point Smooth Variable Structure Filters applications into robotic arm
In this work, well known Sigma Point Kalman Filters (SPKFs); namely Unscented Kalman filter (UKF), the Cubature Kalman filter (CKF), and the Central Differences Kalman filter (CDKF) will be combined to the Smooth Variable Structure Filter (SVSF), in order to create stable and robust algorithms that can be applied to highly non-linear systems. The proposed algorithms will be applied into 4-DOF robotic arm that consists of one prismatic joint and three revolute joints (PRRR). By doing so, the stability of the proposed filters will be guaranteed. Which makes the comparison between the SPKFs easier. These methods will be executed using simulation experiments, and then they will be compared in terms of stability, robustness and the quality of the optimality.