Multi-agent collective behaviors analysis and applications in complex networks and systems

摘要

Cooperative and collective behaviors in networks of multiple autonomous agents have received considerable attention in recent years due to the growing interest in understanding intriguing animal group behaviors, such as flocking and swarming, and also due to their emerging broad applications in sensor networks, UAV (Unmanned Air Vehicles) formations, robotic teams, to name just a few. To coordinate with other agents in a network, every agent needs to share information with its adjacent peers so that all can agree on a common goal of interest. Recently, some progress has been made in analyzing collective behaviors in dynamical networks for which some closely related focal topics are synchronization, consensus, swarming and flocking. In this thesis, the multi-agent collective behaviors (specifically, synchronization, consensus, swarming, and flocking) and some of their potential applications are investigated. In particular, following issues are studied in detail: (a) firstorder consensus in multi-agent systems with nonlinear dynamics; (b) second-order consensus in multi-agent systems with time delays and linear or nonlinear dynamics; (c) higher-order consensus in linear multi-agent dynamical systems; (d) stability analysis of a swarming behavioral model with hybrid nonlinear profiles; (e) distributed leader-follower flocking control for multi-agent dynamical systems with time-varying velocities; (f) adaptive and pinning network controls in complex dynamical systems; (g) applications in estimating uncertain delayed genetic regulatory networks and distributed consensus filtering in sensor networks. The main contributions of this thesis are summarized as follows: (a) a generalized algebraic connectivity framework is proposed to describe the consensus