This study proposes a distributed model predictive control (MPC) strategy to achieve consensus of sampled-data multi-agent systems with double-integrator dynamics. On the basis of the error of state between each agent...
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This study proposes a distributed model predictive control (MPC) strategy to achieve consensus of sampled-data multi-agent systems with double-integrator dynamics. On the basis of the error of state between each agent and the centre of its subsystem, a novel distributed MPC strategy (algorithm 1) is obtained with the exchange of current states only. Then, a reverse iterative algorithm (algorithm 2) is specially designed for the receding horizon optimisation of sampled-data double-integrator dynamics. Illustrative examples are finally displayed to verify the effectiveness and advantage of the distributed MPC consensus strategy and the impact of sampling period on consensus.
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