[CFAR Distinguished Professor Lecture Series]The Robustness and Adaptability of PID Control by Professor Song Yongduan
18 May 2022 | 10:00am (Singapore Time)
PID (Proportional, Integral and Derivative) Control is widely used in engineering systems, and has been viewed as a control method for linear systems with trail and error process for it’s gain determination.
In this talk, Prof Song Yongduan will explore a performance-based method for systematically updating PID gains with guaranteed robustness and adaptability for nonlinear systems.
SPEAKER
Prof Song Yongduan (F'20) received the Ph.D. degree in electrical and computer engineering from Tennessee Technological University, Cookeville, TN, USA, in 1992. He held a tenured Full Professor with North Carolina A\&T State University, Greensboro, NC, USA, from 1993 to 2008 and a Langley Distinguished Professor with the National Institute of Aerospace, Hampton, VA, USA, from 2005 to 2008. He is currently the Dean of the School of Automation, Chongqing University, Chongqing, China. He was one of the six Langley Distinguished Professors with the National Institute of Aerospace (NIA), Hampton, VA, USA, and the Founding Director of Cooperative Systems with NIA. His current research interests include intelligent systems, guidance navigation and control, bio-inspired adaptive and cooperative systems.
Prof Song was a recipient of several competitive research awards from the National Science Foundation, the National Aeronautics and Space Administration, the U.S. Air Force Office, the U.S. Army Research Office, and the U.S. Naval Research Office. He is an IEEE Fellow and has served/been serving as an Associate Editor for several prestigious international journals, including the IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man and Cybernetics, and IEEE Transactions on Developmental and Cognitive Systems, etc.
He is now the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems.