Distributed adaptive consensus algorithm of multi-agent systems with input additive uncertainty

초록

"In this paper, we study a consensus problem for multi-agent systems with input additive uncertainty using the distributed adaptive algorithm. We assume that the input additive uncertainty monotonically increases. Under this assumption, we construct distributed adaptive consensus algorithms that use only relative information of neighboring agents to solve leaderless and leader-following consensus problems. The theoretical analysis shows that the proposed algorithms can solve the consensus problems without knowing a graph topology. Finally, numerical examples are provided to validate the theoretical results. ? ICROS 2018.

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