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Investigates consensus in multi-agent systems, implying improved coordination through impulsive control.
This paper investigates the leader-following mean-square consensus of stochastic linear multi-agent systems (MASs) via distributed impulsive control. A distributed impulsive control law relying on local neighbor information and discrete sampled data is designed. By using Lyapunov theory, stochastic analysis, algebraic graph theory and the linear matrix inequality (LMI) technique, sufficient conditions for mean-square consensus are derived. We provide methods for coupling gains and impulsive control gains and deduce the consensus result of deterministic linear MASs as a corollary. Numerical simulations demonstrate the effectiveness of the proposed control.
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Li et al. (2026) studied this question.
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