报告题目：Lebesgue Approximation Model and Its Applications
报告人：王晓峰博士（University of South Carolina）
时间：2017年7月15日16:00
地点：延长校区电机楼二楼会议室202
报告摘要：
Traditional model-based approaches are based on periodic sampling or approximation, where the model is discretized with a fixed period. Despite the easiness in analysis and design, periodic approaches may be undesirable from the computation-efficiency point of view. This talk presents a cost-efficient Lebesgue-approximation model (LAM) of continuous-time nonlinear systems, where the state iteration is activated on an “as-needed” basis, but not periodically. Sufficient conditions are provided to ensure asymptotic stability and uniformly ultimate boundedness of the LAM. Theoretical bounds are derived to quantify the difference between the states of the LAM and the original continuous-time system. With these results, the LAM is applied to diagnosis, prognosis, and model predictive control. Simulations show that the LAM-based approaches can dramatically reduce the number of iterations without significantly sacrificing the accuracy.
报告人简介：Dr. Xiaofeng Wang is assistant professor in the Department of Electrical Engineering at the University of South Carolina, Columbia (UofSC). He obtained his PhD degree in Electrical Engineering at the University of Notre Dame in 2009 and completed his postdoctoral research in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana and Champaign before joining UofSC. His research interests include robotics, autonomous systems, multi-agent systems, motion planning, networked and real-time control systems, fault-tolerant control, and optimization.
