主讲人 | 陈倩 | 邀请人 | 无 |
讲座时间 | 2023年 4 月 12 日 14:30-15:30 ；本学期第 9 周 | ||
讲座地点 | 管理学院4楼第二会议室报告厅 | ||
讲座题目 | Disturbance rejection control for connected and automated vehicle platoons | ||
主讲人简介 | Qian Chen, lecturer, Transportation System Engineering, Business School, University of Shanghai for Science and Technology. Her research interests include perimeter control, micro-control for connected and automated vehicles, and distributed state estimation in sensor networks. Until now, she has published 5 SCI papers and 2 EI papers and also has 1 invention patent and 2 utility model patents. Besides she has served as a reviewer for several international journals related to traffic engineering, signal processing, sensors and other fields.She was a visiting Ph.D. student with the Department of Civil and Environmental Engineering, University of Wisconsin-Madison from 2019 to 2021. She has won “Scholarship for New Excellent Applicants” of Southeast University, BOSCH AIoT Scholarship, and “Outstanding Reviewer” for the Journal Signal Processing. Besides, she has been selected for the Shanghai 2023 Sailing Program. | ||
讲座摘要 | Growing traffic demand leads to overloading of road infrastructure, which makes large and medium-sized cities prone to traffic congestion. Traffic congestion may bring traffic accidents and exhaust pollution. With the development of information technology, intelligent transportation system has become effective to improve urban traffic operation. Studies have shown that connected and automated vehicle technology can significantly relieve urban traffic congestion. This talk focuses on the application of disturbance rejection control in traffic systems, especially in the longitudinal control of connected vehicles. Main contents: 1. two longitudinal control algorithms for connected and automated vehicle platoons, which can effectively enhance the disturbance rejection performance of connected and automated vehicle platoons, thereby improving the efficiency of the road network; 2. a disturbance observer-based tube model predictive control method with explicit consideration of string stability, which can effectively improve the disturbance rejection performance, driving comfort, and fuel economy. | ||
