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This paper studies the consensus problem of general linear discrete-timemulti-agent systems (MAS) with input constraints and bounded time-varyingcommunication delays. We propose a robust distributed model predictive control(DMPC) consensus protocol that integrates the offline consensus design withonline DMPC optimization to exploit their respective advantages. Moreprecisely, each agent is equipped with an offline consensus protocol, which isa priori designed, depending on its immediate neighbors' estimated states.Further, the estimation errors propagated over time due to inexact neighboringinformation are proved bounded under mild technical assumptions, based on whicha robust DMPC strategy is deliberately designed to achieve robust consensuswhile satisfying input constraints. Moreover, it is shown that, with thesuitably designed cost function and constraints, the feasibility of theassociated optimization problem can be recursively ensured. We further providethe consensus convergence result of the constrained MAS in the presence ofbounded varying delays. Finally, two numerical examples are given to verify theeffectiveness of the proposed distributed consensus algorithm.

A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays