This　study　presents　a　self-triggered　distributed　model　predictive　control　algorithm　for　the　flock　of　a　multi-agent　system.　All　the　agents　in　a　flock　are　endowed　with　the　capability　of　determining　the　sampling　time　adaptively　to　reduce　the　unnecessary　energy　consumption　in　communication　and　control　updates.　The　agents　are　dynamically　decoupled　in　a　flock,　and　each　agent　is　driven　by　a　local　model　predictive　controller,　which　is　designed　by　minimising　the　position　irregularity　between　the　agent　and　its　neighbours,　velocity　tracking　errors　as　well　as　its　control　efforts.　Moreover,　the　collision　avoidance　is　considered　by　introducing　constraints　in　the　model　predictive　minimisation　problem.　In　order　to　adaptively　determine　the　sampling　time,　a　self-triggered　algorithm　is　designed　by　guaranteeing　the　decrease　of　the　Lyapunov　function.　Finally,　numerical　simulations　are　given　to　demonstrate　the　feasibility　of　the　proposed　flocking　algorithm.