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This paper proposes a distributed model predicted control (DMPC) approach forconsensus control of multi-agent systems (MASs) with linear agent dynamics andbounded control input constraints. Within the proposed DMPC framework, eachagent exchanges assumed state trajectories with neighbors and solves a localopen-loop optimization problem to obtain the optimal control input. In theoptimization problem, a discrete-time consensus protocol is introduced intoupdate law design for assumed terminal states, with which asymptotic consensusof assumed terminal states and recursive feasibility are rigorously proved.Together with the optimal cost function, an infinite series of cost-to-gofunctions is introduced into the design of a Lyapunov function, with whichclosed-loop asymptotic consensus is finally proved. Two applications includingcooperation of autonomous underwater vehicles (AUVs) and connected andautomated vehicles (CAVs) are used to validate the effectiveness of theproposed DMPC approach.

Distributed Model Predicted Control of Multi-agent Systems with Applications to Multi-vehicle Cooperation