{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:23:05Z","timestamp":1760145785182,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T00:00:00Z","timestamp":1724112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler\u2013Lagrange method. A trajectory-following control strategy based on the first following point is proposed, and a feedback linearization control algorithm is designed based on the vehicle dynamics model to achieve the trajectory following of the rear vehicle. Based on the target trajectory formed by the first following point and measured by virtual sensors, a vector analysis method grounded in geometric relationships is proposed to solve in real time for the desired position, velocity, and acceleration of the vehicle. Finally, a MATLAB\/SIMPACK dynamics virtual prototype is established to test the vehicle\u2019s trajectory-following effectiveness and dynamics performance under lane change and circular curve routes. The results indicate that the control algorithm can achieve trajectory following while maintaining good vehicle dynamics performance. It is robust to variations in vehicle mass, vehicle speed, tire cornering stiffness, and road friction coefficient.<\/jats:p>","DOI":"10.3390\/s24165385","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:53:19Z","timestamp":1724417599000},"page":"5385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Train Trajectory-Following Control Method Using Virtual Sensors"],"prefix":"10.3390","volume":"24","author":[{"given":"Youpei","family":"Huang","sequence":"first","affiliation":[{"name":"Rail Transit Institute, Tongji University, Shanghai 200333, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoguang","family":"Ma","sequence":"additional","affiliation":[{"name":"Rail Transit Institute, Tongji University, Shanghai 200333, China"},{"name":"CRRC Nanjing Puzhen Co., Ltd., Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihui","family":"Ren","sequence":"additional","affiliation":[{"name":"Rail Transit Institute, Tongji University, Shanghai 200333, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,20]]},"reference":[{"key":"ref_1","first-page":"80","article-title":"Study on transportation capacity of intelligent rail express system","volume":"25","author":"Lei","year":"2022","journal-title":"Urban Rail Transit Res."},{"key":"ref_2","first-page":"27","article-title":"Characteristics and Applicability Analysis of Zhuzhou Smart Rail Experience Line Project of Intelligent Rail Express System","volume":"23","author":"He","year":"2020","journal-title":"Urban Rail Transit Res."},{"key":"ref_3","first-page":"139","article-title":"A multi-axis cooperative pre-tracking control method for virtual rail trains","volume":"46","author":"Cui","year":"2022","journal-title":"J. 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