Distributed Optimization and Resource Allocation of High-Order Nonlinear Multi-Agent Systems

Backstepping, Consensus, and Learning-Based Methods
Erscheinungsjahr 2026
ISBN: 978-981-9253-14-2
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Intelligent Control and Learning Systems

This book provides a unified and practical framework for solving distributed optimization and resource allocation problems in nonlinear multi-agent systems under complex dynamic environments. It enables readers to design control strategies that achieve fast convergence, robustness against disturbances, and resilience to communication and security constraints. The book presents a series of advanced methodologies, including finite-time, fixed-time, and predefined-time optimization control, disturbance rejection via observer-based techniques, and learning-based approaches using adaptive dynamic programming (ADP). It also introduces event-triggered mechanisms to reduce communication burden and observer-driven strategies to address false data injection attacks. These topics are of particular interest as they bridge the gap between theoretical optimization and real-world control implementation. Special features of the book include a unified optimization–control perspective, systematic integration of multiple time-constrained control schemes, and application-oriented case studies such as quadrotor UAV swarm coordination. The presentation combines rigorous theoretical analysis with intuitive explanations, supported by illustrative figures, structured frameworks, and comparative simulations. Readers will gain both theoretical insight and practical tools for addressing optimization and control challenges in networked systems. This book is intended for researchers, graduate students, and engineers in control systems, multi-agent systems, robotics, and intelligent networked systems.

Zielgruppe

Research

Autoren/Hrsg.

Li, Cheng

Yuan, Jiaxin

Ao, Qingxiang

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme

Inhaltsverzeichnis

Introduction.-  Part I Distributed Resource Allocation for High-Order Nonlinear Multi-Agent Systems.- Finite-Time Resource Allocation for Fractional Nonlinear Multi-Agent Systems.- Fixed-Time Resource Allocation for Disturbed High-Order Multi-Agent Systems.- Part II Distributed Optimization and Consensus for High-Order Nonlinear Systems.- Fixed-Time Distributed Optimization for Uncertain High-Order Nonlinear Multi-Agent Systems.- Predefined-Time Optimization Control for Quadrotor UAV Swarms.- Part III ADP-Based Distributed Resource Allocation and Intelligent Coordination.- Event-Triggered ADP for Distributed Resource Allocation in Nonlinear Multi-Agent Systems.- Observer-Driven ADP for Secure Resource Allocation in High-Order Nonlinear Multi-Agent Systems.

Über Autor(innen)

Dr. Cheng Li received the Ph.D. degree in Management Science and Engineering. He is currently a Professor and the Dean of the College of Air Transportation (Flight College) at Shanghai University of Engineering Science, Shanghai, China. He also serves as a Member of the Academic Committee of the university and the Chair of the College Academic Committee. Dr. Li’s research interests include intelligent transportation systems, aviation operations management, and optimization in complex engineering systems. He has extensive experience in academic administration and interdisciplinary research. He has served as Deputy Party Secretary and Vice Dean of the College of Air Transportation (2014–2024), and was a Visiting Scholar at Tongji University from 2013 to 2014. He is actively engaged in professional societies, serving as a Standing Director of the Shanghai Society of Aeronautics, a member of its Academic Committee, an expert in the Shanghai Science and Technology Expert Database, a science popularization expert of the Chinese Society of Aeronautics and Astronautics, and a research fellow of the China Society of Logistics. His work contributes to bridging academic research and engineering applications in aviation and transportation systems.

Dr. Jiaxin Yuan received his M.S. degree from Xi’an Jiaotong University, Xi’an, China, in 2013, and the Ph.D. degree from Shanghai Jiao Tong University, Shanghai, China, in 2018. He was a Visiting Scholar at Shaanxi Normal University in 2022. He is currently an Associate Professor with the Shanghai University of Engineering Science, Shanghai, China. Dr. Yuan’s research focuses on distributed optimization and control of nonlinear multi-agent systems, including resource allocation, time-constrained control, disturbance rejection, and intelligent control of UAV systems. He has published multiple peer-reviewed papers in leading international journals and has been actively involved in academic research and engineering applications in networked control systems. His recent work emphasizes the integration of optimization and control, as well as learning-based approaches for complex dynamic systems.

Qingxiang Ao received his M.S. degree from the College of Air Transportation (Flight College), Shanghai University of Engineering Science, Shanghai, China. He is currently pursuing the Ph.D. degree with the School of Automation, Nanjing University of Science and Technology, Nanjing, China. His research interests include distributed optimization, adaptive dynamic programming, distributed learning algorithms, and control of nonlinear multi-agent systems.