Keynote Speeches

Keynote Speech I

AI and Machine Learning for Optimal Control of Complex Nonlinear Systems

Derong Liu
Guangdong University of Technology
derong@gdut.edu.cn

Researchers have been searching for novel control methods to handle the complexity of modern industrial processes. Artificial intelligence and especially machine learning approaches might provide a solution for the next generation of control methodologies that can handle the level of complexities in many modern industrial processes. It has been shown by many researchers reinforcement learning can do a very good job approximating optimal control actions and provide a nearly optimal solution for the control of complex nonlinear systems. It requires a combination of function approximation structures such as neural networks and optimal control techniques such as dynamic programming. Theoretical development has been on a fast-track in the past ten years. On the other hand, parallel control, cloudcontrol, as well as agent-based control have been studied as alternatives for handling complex nonlinear systems. This lecture will review the development of these methodologies to summarize the inherent relationship among these developments.

Biosketch

Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame in 1994. He was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2016. He has published 19 books. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008, the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014, the INNS Gabor Award in 2018, the IEEE TNNLS Outstanding Paper Award in 2018, the IEEE SMC Society Andrew P. Sage Best Transactions Paper Award in 2018, and the IEEE/CCA J. Automatica Sinica Hsue-Shen Tsien Paper Award in 2019. He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, a Fellow of the International Association of Pattern Recognition, and a Member of Academia Europaea (The Academy of Europe).