Distributed Pinning-Controlled Flocking with Preserved Network Connectivity

Abstract

This chapter introduces distributed pinning-controlled flocking of multi-agent systems with preserved network connectivity. Most existing flocking algorithms rely on information about both relative position and relative velocity among neighboring agents. We first propose a connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements. Under the assumption that the initial interactive network is connected, the flocking algorithm not only can steer a group of agents to a stable flocking motion, but also can preserve the connectivity of the interactive network during the dynamical evolution. Moreover, we investigate the flocking algorithm with a virtual leader and show that all agents can asymptotically attain a desired velocity even if only one agent in the team has access to the information of the virtual leader. We then investigate the flocking problem of multiple nonlinear dynamical mobile agents with a virtual leader in a dynamic proximity network. We assume that only a fraction of agents in the network are informed and propose a connectivity-preserving flocking algorithm. Under the assumption that the initial network is connected, we introduce local adaptation strategies for both the weights on the velocity navigational feedback and the coupling strengths that enable all agents to track the virtual leader, without requiring the knowledge of the agent dynamics. The resulting flocking algorithm works even for the case where only one agent is informed.