Intelligent Optimal Control for Distributed Industrial Systems

Shaoyuan Li is a Ph.D. supervisor, IEEE senior member, and currently works in the Department of Automation Shanghai Jiao Tong University as the distinguished professor, the dean of SJTU-ParisTech Elite Institute of Technology, and the vice director of Key Laboratory of Ministry of Education. Pro. Li receives his Ph.D. degree in Computer and System Science from Nankai University in 1997. Eight books and more than 500 papers have been published in journals/conferences, which described his researching accomplishments. Prof. Li has worked in the area of control theory and engineering for more than 25 years and has worked in the area of distributed model predictive control for more than 13 years. He was awarded "Second Prize of National Natural Science Award" in 2018, "The First-class Awards of the Science and Technology Progress by Shanghai Government" in 2006, and "The 1st Class  CAA Natural Science Award" by Chinese Association of Automation (CAA) in 2017 

Yi Zheng is an assistant professor of Shanghai Jiao Tong University. He is an IEEE, IEEE CSS member, and currently works in the Department of Electrical Engineering, Shanghai Jiao Tong University; he receives his Ph.D. degree in Control Theory and Engineering from Shanghai Jiao Tong University. He was with Shanghai Petrochemical Company, Ltd., Shanghai, from 2000 to 2003 and was with GE-global research (Shanghai) from 2010 to 2012. His interested research fields are the distributed control and optimization of large-scale systems and the application of industrial process. 

Binqiang Xue is an assistant professor of Qingdao University. He currently works in College of Automation, Qingdao University; he receives his Ph.D. degree in Control Theory and Engineering from Shanghai Jiao Tong University. He owns more than ten peer-viewed papers. He has worked in model predictive control and moving horizon state estimation for more than five years.

This book focuses on the distributed control and estimation of large-scale networked distributed systems and the approach of distributed model predictive and moving horizon estimation. Both principles and engineering practice have been addressed, with more weight placed on engineering practice. This is achieved by providing an in-depth study on several major topics such as the state estimation and control design for the networked system with considering time-delay, data-drop, etc., Distributed MPC design for improving the performance of the overall networked system, which includes several classic strategies for different scenarios, details of the application of the distributed model predictive control to smart grid system and distributed water network. The comprehensive and systematic treatment of theoretical and practical issues in distributed MPC for networked systems is one of the major features of the book, which is particularly suited for readers who are interested to learn practical solutions in distributed estimation and optimization of distributed networked systems. The book benefits researchers, engineers, and graduate students in the fields of chemical engineering,  control theory and engineering, electrical and electronic engineering, chemical engineering, and computer engineering, etc. 

Addresses model predictive control for networked system both in theory and practice

Status of Research on Networked Distributed Systems.- Moving horizon state estimation for networked systems with Random Packet Loss.- Design of predictive controller for networked systems.- Moving horizon scheduling for networked systems with communication constraints.- Distributed Predictive Control for local performance index.- Coordinated distributed predictive control system.- Distributed Predictive Control under Communication Constraints.- Application of distributed model predictive control in accelerated cooling process.