  This paper proposes a distributed model predicted control (DMPC) approach for
consensus control of multi-agent systems (MASs) with linear agent dynamics and
bounded control input constraints. Within the proposed DMPC framework, each
agent exchanges assumed state trajectories with neighbors and solves a local
open-loop optimization problem to obtain the optimal control input. In the
optimization problem, a discrete-time consensus protocol is introduced into
update law design for assumed terminal states, with which asymptotic consensus
of assumed terminal states and recursive feasibility are rigorously proved.
Together with the optimal cost function, an infinite series of cost-to-go
functions is introduced into the design of a Lyapunov function, with which
closed-loop asymptotic consensus is finally proved. Two applications including
cooperation of autonomous underwater vehicles (AUVs) and connected and
automated vehicles (CAVs) are used to validate the effectiveness of the
proposed DMPC approach.
