Prof. Zhong-Ping JIANG
Tandon School of Engineering, New York University
Title:Learning to Control Dynamic Systems: Stability and Robustness
Bio: Zhong-Ping JIANG received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris (now, called ParisTech-Mines), France, in 1993, under the direction of Prof. Laurent Praly.
Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, robust adaptive dynamic programming, reinforcement learning and their applications to information, mechanical and biological systems. In these fields, he has written five books and is author/co-author of over 450 peer-reviewed journal and conference papers.
Dr. Jiang has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, a Fellow of the IFAC, a Fellow of the CAA and is among the Clarivate Analytics Highly Cited Researchers.
Abstract: This talk looks at problems at the interface of machine learning and automatic control that are motivated by open challenges arising from cyber-physical systems and computational neuroscience. In particular, it will introduce a new design paradigm, called “Robust Adaptive Dynamic Programming (RADP)”, that is fundamentally different from traditional control theory. In the classical paradigm, controllers are often designed for a given class of dynamical control systems; it is a model-based design. In the RADP paradigm, controllers are learned online using real-time input-output data collected along the trajectories of the control system in question. An entanglement of techniques from reinforcement learning and model-based control theory is advocated to find a sequence of suboptimal controllers that will approximate the optimal solution as learning steps increase. Rigorous stability and robustness analysis can be derived for the closed-loop system with real-time learning-based controllers. The effectiveness of RADP as a new framework for data-driven nonlinear control design is demonstrated via its applications to electric power systems, autonomous vehicles, and biological motor control.
Prof. Hamid A. Toliyat
Texas A&M University, USA
Title: to be updated
Bio: Prof. Toliyat received the B.S, degree from Sharif University of Technology, Tehran, Iran in 1982, the M.S. degree from West Virginia University, Morgantown, WV in 1986, and the Ph.D. degree from University of Wisconsin-Madison, Madison, WI in 1991, all in electrical engineering. Following receipt of the Ph.D. degree, he joined the faculty of Ferdowsi University of Mashhad, Mashhad, Iran as an Assistant Professor of Electrical Engineering. In March 1994 he joined the Department of Electrical and Computer Engineering, Texas A&M University where he is currently Raytheon endowed professor of electrical engineering.
Dr. Toliyat has received the prestigious Nikola Tesla Field Award for “outstanding contributions to the design, analysis and control of fault-tolerant multiphase electric machines” from IEEE in 2014, the Cyrill Veinott Award in Electromechanical Energy Conversion from the IEEE Power Engineering Society in 2004, Patent and Innovation Award from Texas A&M University System Office of Technology Commercialization’s in 2018, 2016 and 2007, TEES Faculty Fellow Award in 2006, Distinguished Teaching Award in 2003, E.D. Brockett Professorship Award in 2002, Eugene Webb Faculty Fellow Award in 2000, and Texas A&M Select Young Investigator Award in 1999. He has also received the Space Act Award from NASA in 1999, and the Schlumberger Foundation Technical Awards in 2001 and 2000.
Prof. Toliyat work is highly cited by his colleagues more than 24,000 times and has an H-index of 78. Dr. Toliyat was an Editor of IEEE Transactions on Energy Conversion. He was Chair of the IEEE-IAS Industrial Power Conversion Systems Department of IEEE-IAS, and is a member of Sigma Xi. He is a fellow of the IEEE, the recipient of the 2008 Industrial Electronics Society Electric Machines Committee Second Best Paper Award as well as the recipient of the IEEE Power Engineering Society Prize Paper Awards in 1996 and 2006, and IEEE Industry Applications Society Transactions Third Prize Paper Award and Second Prize Paper Award in 2006 and 2016, respectively. His main research interests and experience include analysis and design of electrical machines, variable speed drives for traction and propulsion applications, fault diagnosis of electric machinery, and magnetic gear integrated electric machines. Prof. Toliyat has supervised more than 110 graduate students, post docs, and research engineers. He has published around 500 technical papers, presented more than 95 invited lectures all over the world, and has 26 issued and pending US patents. He is the author of 10 books and book chapters including DSP-Based Electromechanical Motion Control, CRC Press, 2003, the co-editor of Handbook of Electric Motors - 2nd Edition, Marcel Dekker, 2004, and the co-author of Electric Machines – Modeling, Condition Monitoring, and Fault Diagnosis, CRC Press, Florida, 2013.
He was the General Chair of the 2005 IEEE International Electric Machines and Drives Conference in San Antonio, Texas. Dr. Toliyat is a Professional Engineer in the State of Texas.
Abstract: to be updated