報告題目：Boundary Control of Freeway Traffic Congestion
報告人：Huan Yu,University of California, San Diego（美國加利福尼亞大學圣地亞哥分校）
主持人：楊飛生 西工大信息物理系統控制與安全研究所(iCPS2)副所長
報告時間：2019年9月26日（周四）上午10:00
報告地點：自動化學院341會議室
報告簡介：
Studies on control of transportation systems have undergone several waves of advancing both theories and engineering techniques towards more intelligent traffic management infrastructure systems. One challenge inhibiting the development of effective transportation management system is caused by disparity of core theories and engineering techniques of two academic fields: control system engineering and transportation science. My talk managed to fill in this gap by discussing control problems of three long-standing freeway traffic congestions: stop-and-go traffic, traffic shockwave and traffic bottlenecks. I developed a systematic partial differential equation (PDE) model-based approach for the boundary control and estimation of freeway traffic congestion problem. The three problems present as distinct PDE models and require the advancement and application of different classical PDE control techniques including backstepping method, predictor feedback design and extremum seeking.
報告人簡歷：
Dr. Huan Yu received her Ph.D. degree in Mechanical and Aerospace Engineering from University of California, San Diego (UCSD). She holds now a postdoctoral position with Professor Miroslav Krstic at Cymer Center for Control systems and Dynamics at UCSD, where she is working on the implementation of PDE control laws for transportation systems. She was a visiting researcher in the Institute of Transportation Studies at University of California, Berkeley in 2018. She is currently a visiting scholar in Resilience Infrastructure Lab at Massachusetts Institute of Technology. Her research interests include modeling and control of distributed parameter systems with applications to transportation systems. Her research involves adaptive and robust control of hyperbolic PDE systems, control of delay systems, extremum seeking control, reinforcement learning, control of traffic network and interconnected systems.
