Distributed model predictive control for consensus of sampled‐data multi‐agent systems with double‐integrator dynamics

L Zhou, S Li - IET Control Theory & Applications, 2015 - Wiley Online Library
L Zhou, S Li
IET Control Theory & Applications, 2015Wiley Online Library
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 …
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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