Flocking of Multi-Agent Systems with Nonuniform and Nonconvex Input Constraints

Abstract

This paper investigates the nonuniform and nonconvex input constrained flocking control problem of continuous-time multi-agent systems. A distributed flocking control algorithm is proposed for each agent using the local information from its neighbor agents subject to nonuniform and nonconvex control constraints. Based on a constraint scaling factor and a specific coordinate transformation, a Lyapunov function is constructed to address the nonlinearities caused by input constraints. It is proved that all agents from an explicitly specified region of initial states eventually converge to a common point with the same velocity, while each agent's inputs remain within its own constrained nonconvex set. A simulation example is given to demonstrate the effectiveness of the theoretical results.