Distributed model predictive control for consensus of sampled‐data multi‐agent systems with double‐integrator dynamics
Lifeng Zhou, Shaoyuan Li · IET Control Theory and Applications · 2015
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.