A discrete-time flocking algorithm for agents with sampled-data double-integrator dynamics
Brandon J. Wellman, Jesse B. Hoagg · 2017
We present a multi-agent control method that addresses flocking in discrete time. The method is decentralized, that is, each agent's controller relies on local sensing to determine the relative positions and velocities of nearby agents. Each agent has the discrete-time double-integrator dynamics obtained by sampling the continuous-time double integrator and applying a zero-order hold on the control input. We demonstrate with analysis and simulations that agents using the discrete-time flocking method converge to a set of flocking formations.