Sampled-data Control of Logical Networks

Yang Liu (Senior Member, IEEE) is currently a Professor at the College of Mathematics and Computer Science, Zhejiang Normal University, China. He received his B.S. degree in mathematics from Zhejiang Normal University, Zhejiang, China, in 2003, and his Ph.D. degree from Tongji University, Shanghai, in 2008. He has authored over 100 publications and two books. He is an Associate Editor of Neural Processing Letters (Springer). He was recognized by Elsevier as a Most Cited Chinese Researcher in 2020-2021, and by Clarivate Analytics as a Highly Cited Researcher in 2019-2021. He was the supervisor of the ICCM Best Paper Award in 2016 and 2022. His research interests include distributed optimization, hybrid systems and logical systems.Jianquan Lu (Senior Member, IEEE) is currently a Professor at the Department of Systems Science, School of Mathematics, Southeast University, Nanjing, China. He received his B.S. degree in mathematics from Zhejiang Normal University, Zhejiang, China, in 2003, his M.S. degree in mathematics from Southeast University, Nanjing, China, in 2006, and his Ph.D. degree in applied mathematics from City University of Hong Kong in 2009. From 2010 to 2012, he was an Alexander von Humboldt Research Fellow at the PIK, Germany. His current research interests include collective behavior in complex dynamical networks and multi-agent systems, logical networks, and hybrid systems. He has published over 90 papers in refereed international journals. Dr. Lu was named a Highly Cited Researcher by Clarivate Analytics from 2018 for three consecutive years, and he was elected one of the Most Cited Chinese Researchers by Elsevier in 2014-2019. He was part of the Program for New Century Excellent Talents in University by The Ministry of Education, China in 2010, and won the Second Award of Jiangsu Provincial Progress in Science and Technology in 2016 as the First Project Member, and the First Award of Jiangsu Provincial Progress in Science and Technology in 2010 asthe Second Project Member. Dr. Lu is an associate editor of Neural Processing Letters (Springer), Journal of Franklin Institute (Elsevier), and Neural Computing and Applications (Springer), and a guest editor of Science China: Information Sciences, Mathematics and Computers in Simulation (Elsevier) and IET Control Theory & Applications.Liangjie Sun is currently pursuing her Ph.D. degree at the Department of Mathematics, The University of Hong Kong. She received her B.S. degree in mathematics from Zhejiang Normal University, Zhejiang, China, in 2016, and her M.S. degree in mathematics from Southeast University, Nanjing, China, in 2019. Her undergraduate thesis: “On the Control Theory of Logical Systems: An Approach of Semi-Tensor Product of Matrices” won the New World Mathematics Awards, Bachelor Thesis Awards - Silver Prize in 2016. Her master’s thesis: “Sampled-data control of Boolean networks and some related qualitative problems” won the outstanding master’s degree award of Jiangsu Province in 2020. Her current research interests include logical dynamics systems and computational biology.

• Chapter 1 Introduction.- Chapter 2 Stabilization of sampled-data Boolean control networks.- Chapter 3 Controllability, observability and synchronization of sampled-data Boolean control networks.- Chapter 4 Stabilization of probabilistic Boolean control networks under sampled-data control.- Chapter 5 Stabilization of aperiodic sampled-data Boolean control networks.- Chapter 6 Event-triggered control for logical control networks.

“The key feature of the book is the conversion of the dynamics of a control network to its algebraic representation based on the so-called semi-tensor product (STP) of matrices. ... Many numerical examples with real-world motivations are prepared to help readers digest the theoretical results.” (Qianchuan Zhao, Mathematical Reviews, February, 2024)“In this book, the authors investigate sampled-data control of logical networks. More precisely, they cover the most updated results on sampled-data control of Boolean networks, including the authors’ contributions. In addition, many illustrative examples are provided.” (Savin Treanţă, zbMATH 1519.93003, 2023)