报告题目：Boundary Control of Freeway Traffic Congestion

报告人：Huan Yu,University of California, San Diego（美国加利福尼亚大学圣地亚哥分校）

主持人：杨飞生 西工大信息物理系统控制与安全研究所(iCPS2)副所长

报告时间：2019年9月26日（周四）上午10:00

报告地点：自动化学院341会议室

报告简介：

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.

报告人简历：

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.