Ngoc Pham Van Bach, T. Duy, Khanh Bui Quoc, T. Trung
{"title":"四自由度汽车运动模拟器的动力学模型及SMCNN控制器设计","authors":"Ngoc Pham Van Bach, T. Duy, Khanh Bui Quoc, T. Trung","doi":"10.1109/ICCAR49639.2020.9107999","DOIUrl":null,"url":null,"abstract":"This research briefly presents the model of four degrees of freedom (4-DOF) car motion simulators using the parallel and serial mechanism. Firstly, the kinematic and dynamics model of 4-DOF are presented. The actual model of the simulator is often deficient in the system's parameters or has the nonlinear uncertainties. Therefore a Sliding Model Control base on artificial intelligence neural network is proposed to approximate the uncertain elements and ensure the stability of the system at the same time. The stability of the system is proved based on Lyapunov theorem. Finally, simulation results verify the effectiveness and accuracy of the proposed algorithm and the comparison between using neural network and not using this element indicates the superiority of the proposed controller.","PeriodicalId":412255,"journal":{"name":"2020 6th International Conference on Control, Automation and Robotics (ICCAR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Dynamics Model and Design of SMCNN Controller for 4DOF Car Motion Simulator\",\"authors\":\"Ngoc Pham Van Bach, T. Duy, Khanh Bui Quoc, T. Trung\",\"doi\":\"10.1109/ICCAR49639.2020.9107999\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research briefly presents the model of four degrees of freedom (4-DOF) car motion simulators using the parallel and serial mechanism. Firstly, the kinematic and dynamics model of 4-DOF are presented. The actual model of the simulator is often deficient in the system's parameters or has the nonlinear uncertainties. Therefore a Sliding Model Control base on artificial intelligence neural network is proposed to approximate the uncertain elements and ensure the stability of the system at the same time. The stability of the system is proved based on Lyapunov theorem. Finally, simulation results verify the effectiveness and accuracy of the proposed algorithm and the comparison between using neural network and not using this element indicates the superiority of the proposed controller.\",\"PeriodicalId\":412255,\"journal\":{\"name\":\"2020 6th International Conference on Control, Automation and Robotics (ICCAR)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 6th International Conference on Control, Automation and Robotics (ICCAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAR49639.2020.9107999\",\"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 6th International Conference on Control, Automation and Robotics (ICCAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAR49639.2020.9107999","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamics Model and Design of SMCNN Controller for 4DOF Car Motion Simulator
This research briefly presents the model of four degrees of freedom (4-DOF) car motion simulators using the parallel and serial mechanism. Firstly, the kinematic and dynamics model of 4-DOF are presented. The actual model of the simulator is often deficient in the system's parameters or has the nonlinear uncertainties. Therefore a Sliding Model Control base on artificial intelligence neural network is proposed to approximate the uncertain elements and ensure the stability of the system at the same time. The stability of the system is proved based on Lyapunov theorem. Finally, simulation results verify the effectiveness and accuracy of the proposed algorithm and the comparison between using neural network and not using this element indicates the superiority of the proposed controller.