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Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems

We consider the problem of steering a multi-agent system to consensus in their outputs. The agents’ dynamics are assumed to be heterogeneous, linear, discrete-time and subject to local convex state and input constraints. We present a sequential distributed model predictive control algorithm that asymptotically steers the agents to consensus in their outputs. In their respective model predictive control problems, the agents minimise the distance of a local target output to those of their neighbours while simultaneously tracking the corresponding target steady-state and input pair. We only require the exchange of these target outputs in the scheme whereas the current state and entire predicted trajectories are not shared.