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Control and optimization methods for complex system resilience

Record Type:	Electronic resources : Monograph/item
Title/Author:	Control and optimization methods for complex system resilienceby Chao Zhai.
Author:	Zhai, Chao.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xx, 206 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Automatic control.
Online resource:	https://doi.org/10.1007/978-981-99-3053-1
ISBN:	9789819930531$q(electronic bk.)

Control and optimization methods for complex system resilience
Zhai, Chao.

Control and optimization methods for complex system resilience[electronic resource] /by Chao Zhai. - Singapore :Springer Nature Singapore :2023. - xx, 206 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v. 4782198-4190 ;. - Studies in systems, decision and control ;v.3..

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.

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.

ISBN: 9789819930531$q(electronic bk.)

Standard No.: 10.1007/978-981-99-3053-1doiSubjects--Topical Terms:

182078
Automatic control.

LC Class. No.: TJ213

Dewey Class. No.: 629.8

Control and optimization methods for complex system resilience
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520 $a 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.
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