Abstract
In this talk, I will introduce a series of data-based methods to analyze the characteristics of linear discrete time-delay systems which have unknown parameter matrices. These characteristics include the state stability, bounded-input bounded-state (BIBS) stability, bounded-input bounded-output (BIBO) stability, state controllability, state observability and output controllability. These data-based methods first transform the system dynamic model into an augmented state space model, and then use the measured historical and current data to construct the system matrix, state controllability matrix, state observability matrix and output controllability matrix of this augmented model, in order to analyze the corresponding characteristics. The advantages of our methods are three folds. First, they can directly verify the system properties according to measured data without the need to know system parameters. Second, their calculation precision is higher than traditional approaches, which need to identify the unknown parameter matrices. Third, our methods have lower computational complexities when constructing the controllability and observability matrices.
Biography
Zhuo Wang received the B.E. degree in automation from Beihang University, Beijing, China, in 2006; and the Ph.D. degree in electrical and computer engineering from University of Illinois at Chicago, Chicago, Illinois, USA, in 2013.
Zhuo Wang was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Alberta, from 2013 to 2014. He worked as a Research Assistant Professor with the Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, from 2014 to 2015. He was selected for the ``Thousand Talents Program for Distinguished Young Scholars'' by the Organization Department of the CPC Central Committee.
Zhuo Wang is currently a Professor and a Ph.D. Instructor with the School of Instrumentation Science and Optoelectronics Engineering, Beihang University. Prof. Wang is a Member of the Adaptive Dynamic Programming and Reinforcement Learning Technical Committee of IEEE Computational Intelligence Society, a Member of the Data Driven Control, Learning & Optimization Professional Committee of CAA, and is a Member of the Fault Diagnosis & Safety for Technical Processes Professional Committee of CAA. He is an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, an Associate Editor of Control Theory & Applications, and is an Associate Editor of Pattern Recognition and Artificial Intelligence.
