Cooperative Control Of Complex Network Systems With Dynamic Topologies
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Cooperative Control of Complex Network Systems with Dynamic Topologies
Far from being separate entities, many social and engineering systems can be considered as complex network systems (CNSs) associated with closely linked interactions with neighbouring entities such as the Internet and power grids. Roughly speaking, a CNS refers to a networking system consisting of lots of interactional individuals, exhibiting fascinating collective behaviour that cannot always be anticipated from the inherent properties of the individuals themselves. As one of the most fundamental examples of cooperative behaviour, consensus within CNSs (or the synchronization of complex networks) has gained considerable attention from various fields of research, including systems science, control theory and electrical engineering. This book mainly studies consensus of CNSs with dynamics topologies - unlike most existing books that have focused on consensus control and analysis for CNSs under a fixed topology. As most practical networks have limited communication ability, switching graphs can be used to characterize real-world communication topologies, leading to a wider range of practical applications. This book provides some novel multiple Lyapunov functions (MLFs), good candidates for analysing the consensus of CNSs with directed switching topologies, while each chapter provides detailed theoretical analyses according to the stability theory of switched systems. Moreover, numerical simulations are provided to validate the theoretical results. Both professional researchers and laypeople will benefit from this book.
Neural Information Processing
The six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields.
Cooperative Control of Nonlinear Multiagent Systems
Cooperative Control for Nonlinear Multiagent Systems: Passivity-Based and Non-Passivity-Based Approaches focuses on the cooperative control of nonlinear multiagent systems containing passivity-based or non-passivity-based consensus, lag consensus, and formation control of multiagent systems. The book provides professional and convenient guidance for those who want to know basic knowledge, advancements, and processes for designing and analyzing cooperative control for nonlinear multiagent systems.Currently, there are a lack of reference titles that systematically introduce students, researchers, and technologists to the backgrounds, developments, and designs protocols for cooperative control (contains passivity-based or not passivity-based consensus, lag consensus, formation control) of nonlinear multiagent systems. - Constructs the frameworks of passivity analysis, consensus control, lag consensus control, and formation control for multiagent systems - Helps readers learn novel control methods - Includes systematic introductions and detailed implementations on how control protocols solve problems in nonlinear multiagent systems