Prof. Zhongsheng Hou
Qingdao University, China
Biography
Prof. Zhongsheng Hou received his Ph.D. degree from Northeastern University in 1994 in Shenyang, China. From 1997 to 2018, he was with Beijing Jiaotong University, where he served as Distinguished Professor, Founding Director of the Advanced Control Systems Lab, and Head of the Department of Automatic Control. From 2002 to 2003, he was a visiting professor at Yale University, United States. He is currently a Chair Professor at Qingdao University, Qingdao, China.
Prof. Hou is the Founding Director of the Technical Committee on Data Driven Control, Learning and Optimization (DDCLO) of the Chinese Association of Automation (CAA), and of the Technical Committee on Data Driven Control Systems of the Asian Control Association. He is an IEEE Fellow, CAA Fellow, and AAIA Fellow.
He was Leading Guest Editor for two special sections on data-driven control in IEEE Transactions on Neural Networks in 2011 and IEEE Transactions on Industrial Electronics in 2017.
His research interests include data-driven control, model-free adaptive control, data-driven learning control, and intelligent transportation systems. He has authored or co-authored more than 300 journal papers, including more than 100 IEEE Transactions papers, and two monographs: Nonparametric Model and its Adaptive Control Theory and Model Free Adaptive Control: Theory and Applications.
His pioneering work on Model-Free Adaptive Control (MFAC) theory has been verified through more than 30 years of academic research and practical applications in more than 300 fields, including wide-area power systems, lateral control of autonomous vehicles, and temperature control of silicon rods.
Abstract
This talk is organized in three parts. The first part focuses on the historical background of model-free adaptive control (MFAC).
The second part presents progress in MFAC theory and applications, as well as the relationship between MFAC, traditional adaptive control, and classical PID control.
The final part presents concluding remarks on the current state and relevance of MFAC theory.