Control and Optimization Methods for Complex System Resilience

2023
ISBN: 978-981-99-3052-4
Verlag: Springer Nature Singapore

Buch, Englisch, Band 478, 206 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 512 g

Reihe: Studies in Systems, Decision and Control

This book provides a systematic framework to enhance the ability of complex dynamical systems in risk identification, security assessment, system protection, and recovery with the assistance of advanced control and optimization technologies. By treating external disturbances as control inputs, optimal control approach is employed to identify disruptive disturbances, and online security assessment is conducted with Gaussian process and converse Lyapunov function. Model predictive approach and distributed optimization strategy are adopted to protect the complex system against critical contingencies. Moreover, the reinforcement learning method ensures the efficient restoration of complex systems from severe disruptions. This book is meant to be read and studied by researchers and graduates. It offers unique insights and practical methodology into designing and analyzing complex dynamical systems for resilience elevation.

Zielgruppe

Research

Autoren/Hrsg.

Zhai, Chao

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik

Inhaltsverzeichnis

Introduction to Complex System Resilience.- Optimal Control Approach to Identifying Cascading Failures.- Jacobian-free Newton-Krylov Method for Risk Identification.- Security Monitoring using Converse Lyapunov Function.- Online Gaussian Process Learning for Security Assessment.- Risk Identification of Cascading Process under Protection.- Model Predictive Approach to Preventing Cascading Process.- Robust Optimization Approach to Uncertain Cascading Process.- Cooperative Control Methods for Relieving System Stress.- Distributed Optimization Approach to System Protection.- Reinforcement Learning Approach to System Recovery.- Summary and Future Work.

Über Autor(innen)

Chao Zhai received the Bachelor's degree in automation engineering from Henan University in 2007 and earned the Master's degree in control theory and control engineering from Huazhong University of Science and Technology in 2009. He received the Ph.D. degree in complex system and control from the Institute of Systems Science, Chinese Academy of Sciences, Beijing, China, in June 2013. From July 2013 to August 2015, he was a post-doctoral fellow at the University of Bristol, Bristol, UK. He is a full professor at the School of Automation, China University of Geosciences, Wuhan, China. His research interests include multi-agent cooperative control, resilient system, social motor coordination, and geohazard monitoring. He is a senior member of IEEE.