Adaptive Nussbaum Design for Nonlinear Dynamical Systems

This book focuses on the challenges of stability analysis and control synthesis for nonlinear dynamical systems, including stochastic systems, multi-agent systems, and nonholonomic systmes. With the adaptive Nussbaum design, this book investigates the asymptotic stabilization problem for nonlinear systems, addressing practical constraints such as physical limitations on system states to ensure safety. Furthermore, this book develops fixed-time stable design to enhance the dynamic performance, with strategies compensating for modeling inaccuracies and external disturbances. In addition, the adaptive Nussbaum design is extended to distributed bipartite consensus design for multi-agent systems and resilient feedback design for nonholonomic systems subject to false data injection. Given the scope, the book offers an essential reference guide for researchers and engineers working on advanced nonlinear system control, as well as a valuable resource for upper-level undergraduate and graduate students.

Yongliang Yang received the B.S. degree in electrical engineering from Hebei University, Baoding, China, in 2011, and the Ph.D. degree in control theory and control engineering from the University of Science and Technology Beijing (USTB), Beijing, China, in 2018. From 2015 to 2017, he was a Visiting Scholar with the Missouri University of Science and Technology, Rolla, MO, USA, sponsored by China Scholarship Council. He was an Assistant Professor with USTB from 2018 to 2020. From 2020, he was UM Macao Fellow with the State Key Laboratory of Internet of Things for Smart City, Faculty of Science and Technology, University of Macau. He was the Marie Skłodowska-Curie Research Fellow with the School of Engineering, University of Warwick from 2023. He is currently an Associate Professor with USTB. His research interests include reinforcement learning theory, robotics, distributed optimization and control for cyber-physical systems. He was a recipient of the Best Ph.D. Dissertation of the China Association of Artificial Intelligence, the Best Ph.D. Dissertation of USTB, Chancellor's Scholarship in USTB, Excellent Graduates Awards in Beijing, UM Macao Talent Program in Macau, and MSCA Fellowship in Europe. He is an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems.

Guilong Liu received the B.E. degree in automation in 2022 and the M.E. degree in control science and technology in 2025 from University of Science and Technology Beijing, Beijing, China. His current research interests include intelligent control for robots.

Qing Li received the B.E. degree in industrial electrical automation from the North China University of Science and Technology, Tangshan, China, in 1993, and the Ph.D. degree in control theory and its applications from the University of Science and Technology Beijing, Beijing, China, in 2000. He is currently a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He has been a Visiting Scholar with Ryerson University, Toronto, ON, Canada, from February 2006 to February 2007. His research interests include intelligent control and intelligent optimization.