Distributed Robust Model Predictive Consensus Control for Multiagent Systems Subject to Uncertainties and Communication Delays

For linear multi-agent systems (MASs) with parameter uncertainties, external disturbances, and time-varying communication delays, a distributed robust model predictive consensus tracking control method is proposed in this paper. First, through system modeling, a min-max stabilization problem is formulated towards the consensus tracking control objective. Subsequently, a Lyapunov-Razumikhin functional (LRF) is constructed. By analyzing conditions such as input-to-state stability (ISS) and robust positive invariance (RPI), an optimization framework constrained by a sequence of linear matrix inequalities (LMIs) is yielded, which can be solved online iteratively to synchronously update control policies for all agents, thereby ensuring system-wide stability and consensus attainment. The efficacy of the presented approach is validated through a series of simulations.