{"title":"封面图片，第五卷，第2期，2025年6月","authors":"","doi":"10.1002/msd2.70036","DOIUrl":null,"url":null,"abstract":"<p><b>Front Cover Caption: Control of a lambda-robot based on machine learning surrogates for inverse kinematics and kinetics:</b> Tracking control of multibody systems with closed-loop mechanisms presents significant computational challenges due to the complexity of inverse kinematics and dynamics. This study introduces an innovative approach that replaces traditional model-based methods with artificial intelligence by training surrogate models on simulation data. Using the λ-robot, a parallel mechanism, as a case study, the workspace is analyzed to ensure comprehensive data coverage for training. The trained surrogates provide control inputs that enable the use of a linear quadratic regulator (LQR) for trajectory tracking. An additional feedback loop addresses model uncertainties. Simulation results validate the effectiveness of this AI-enhanced, data-driven control framework.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":60486,"journal":{"name":"国际机械系统动力学学报(英文)","volume":"5 2","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.70036","citationCount":"0","resultStr":"{\"title\":\"Cover Image, Volume 5, Number 2, June 2025\",\"authors\":\"\",\"doi\":\"10.1002/msd2.70036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Front Cover Caption: Control of a lambda-robot based on machine learning surrogates for inverse kinematics and kinetics:</b> Tracking control of multibody systems with closed-loop mechanisms presents significant computational challenges due to the complexity of inverse kinematics and dynamics. This study introduces an innovative approach that replaces traditional model-based methods with artificial intelligence by training surrogate models on simulation data. Using the λ-robot, a parallel mechanism, as a case study, the workspace is analyzed to ensure comprehensive data coverage for training. The trained surrogates provide control inputs that enable the use of a linear quadratic regulator (LQR) for trajectory tracking. An additional feedback loop addresses model uncertainties. Simulation results validate the effectiveness of this AI-enhanced, data-driven control framework.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":60486,\"journal\":{\"name\":\"国际机械系统动力学学报(英文)\",\"volume\":\"5 2\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.70036\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"国际机械系统动力学学报(英文)\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/msd2.70036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"国际机械系统动力学学报(英文)","FirstCategoryId":"1087","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/msd2.70036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Front Cover Caption: Control of a lambda-robot based on machine learning surrogates for inverse kinematics and kinetics: Tracking control of multibody systems with closed-loop mechanisms presents significant computational challenges due to the complexity of inverse kinematics and dynamics. This study introduces an innovative approach that replaces traditional model-based methods with artificial intelligence by training surrogate models on simulation data. Using the λ-robot, a parallel mechanism, as a case study, the workspace is analyzed to ensure comprehensive data coverage for training. The trained surrogates provide control inputs that enable the use of a linear quadratic regulator (LQR) for trajectory tracking. An additional feedback loop addresses model uncertainties. Simulation results validate the effectiveness of this AI-enhanced, data-driven control framework.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
