Consensus and rendezvous predictive control for multi-agent systems with input constraints

The discrete-time double-integrator consensus and rendezvous problems are both addressed for distributed multi-agent systems with directed switching topologies and input constraints. We develop model predictive control algorithms to achieve stable consensus or rendezvous provided that the proximity nets always have a directed spanning tree and the sampling period is sufficiently small. Moreover, the control horizon is extended to larger than one as well, which endows sufficient degrees of freedom to facilitate controller design. Numerical simulations are finally conducted to show the effectiveness of the control algorithms.