Model predictive control algorithm for consensus in multi-agent systems under dynamically changing interaction networks

This paper addresses model predictive control (MPC) schemes for consensus in multi-agent systems (MASs) with discrete-time single-integrator dynamics under switching directed interaction graphs. The control horizon is extended to more than one which endows the closed-loop system with extra degree of freedom. We propose a concise stability analytical method for the MPC scheme and derive sufficient conditions related to the sampling period and the proximity topology to achieve consensus by using the properties of stochastic matrices. Finally, numerical simulations are conducted to illustrate the effectiveness of the theoretical results.