Adaptive Predefined-Time Attitude Control For Spacecraft

Adaptive Predefined-Time Attitude Control for Spacecraft presents the latest advancements in spacecraft control dynamics, with a particular focus on time-bound strategies that guarantee rapid and smooth system stabilization under realistic mission constraints. Rooted in the expertise of scholars with extensive experience in nonlinear and adaptive control, the book establishes a solid theoretical foundation in finite-time and predefined-time formulations before transitioning to sophisticated techniques such as fuzzy logic, dynamic surface control, neural networks, and event-triggered design. Subsequent chapters broaden the scope to encompass multi-spacecraft coordination and time-triggered adaptation, reflecting the growing trend toward autonomy and intelligent systems in modern aerospace applications.

Readers are guided through a cohesive suite of state-of-the-art methodologies, along with insights into emerging trends and future frontiers, all engineered to optimize reliability, efficiency, and fault tolerance. Graduate students, early-career researchers, and experienced engineers in both academia and industry will find this volume a comprehensive and indispensable reference for the design and deployment of intelligent attitude control systems in modern flight and satellite missions.

• Enhances overall spacecraft performance through intelligent control strategies, such as neural networks and finite-time control, ensuring robust operation in highly complex and uncertain environments
• Delivers precise and reliable attitude stabilization using advanced techniques like fixed-time sliding mode control and predefined-time backstepping, maintaining accuracy even in the presence of dynamic external disturbances
• Ensures fault resilience and mission continuity by employing adaptive and event-triggered control methods that respond proactively to unexpected system failures, preserving functionality without compromising performance
• Promotes control efficiency and autonomy by integrating real-time decision-making frameworks and low-complexity algorithms that enable spacecraft to adapt to evolving mission conditions with minimal ground intervention

Qiang Chen received the B.S. degree in measure and control technology and instrumentation from Hebei Agricultural University, Baoding, China, in 2006, and the Ph.D. degree in control science and engineering from Beijing Institute of Technology, Beijing, China, in 2012. Since 2012, he has been with the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, where he was a Professor in 2022. He has published over 100 peer-reviewed papers in journals and conference proceedings. He has been authorized more than 60 invention patents, 13 of which were transferred. His research interests include adaptive control and iterative learning control with application to motion control systems.

Shuzong Xie received the M.Eng., and Ph.D. degrees in control science and engineering from the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China, in 2018, and 2022, respectively. From 2022 to 2024, he was a Postdoctoral Fellow with the Department of Control Science and Engineering, Zhejiang University, Hangzhou, China. Since 2024, he has been an Assistant Professor with the School of Automation and Electrical Engineering, Zhejiang University of Science and Technology. His current research interests include adaptive control and finite-time control with applications to spacecraft attitude systems and wind energy conversion systems.

Xiongxiong received the M.S. degree in operation and control from Qufu Normal University, Qufu, China, in 1994, and the Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou, China, in 1997. He held a post-doctoral position with Harbin Institute of Technology from 1998 to 2000. He joined Zhejiang University of Technology in 2001, where he has been a Professor with the College of Information Engineering.

Shubo Wang received the Ph.D. degree in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 2017. From 2017 to 2024, he was with the School of Automation, Qingdao University, Qingdao, China, where he became a Full Professor in 2023. He has been with the Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, since 2024. He has coauthored one monograph and more than 70 international journal and conference papers. His current research interests include adaptive control, parameter estimation, neural network, servo system, robotic, nonlinear control, and applications for robotics and motor driving systems.