时间：9月7日（周一）9:30
地点：正阳楼3号楼102
主讲人：Mattia Zorzi 帕多瓦大学信息工程学院 副教授
主讲人简介：Mattia Zorzi received the M.S. degree in Automation Engineering and the Ph.D. degree in Information Engineering from the University of Padova, Padova, Italy, in 2009 and 2013, respectively. He held postdoctoral appointments with the Department of Electrical Engineering and Computer Science, University of Liege, Liege, Belgium, and with the Human Inspired Technology Research Centre, University of Padova, Padova, Italy. He held visiting positions with the Department of Electrical and Computer Engineering, University of California, Davis, USA, and with the Department of Engineering, University of Cambridge, Cambridge, U.K., in 2011 and 2013-2014, respectively. He is currently an Associate Professor with the Department of Information Engineering, University of Padova. His current research interests include machine learning, deep learning, robust estimation, identification theory.
摘要：经典的卡尔曼滤波，虽已广泛应用于导航、机器人、金融以及环境监测等领域，然而该方法通常依赖于精确的状态空间模型信息。事实上，在许多实际场景中，系统模型的信息往往只能近似获得，并且不可避免地受到各种不确定性因素的影响。显然，在这种情况下，经典的滤波方**使估计性能有所下降，甚至出现系统不稳定现象。本次报告将介绍一种基于极小极大博弈论方法的鲁棒状态估计框架。该方法将模型不确定性视为一个“对抗参与者”，并通过设计估计器，使其在最不利情形下的估计误差最小化，从而提高状态估计对模型不确定性的鲁棒性。随后，报告将进一步讨论该方法向非线性系统的推广。最后，报告还将探讨如何利用观测数据学习系统的不确定性信息，进而建立针对最坏情形下的鲁棒设计与数据驱动自适应方法之间的联系。
