On Consensus Control of Uncertain Multiagent Systems Based on Two Types of Interval Observers

In this article, we investigate the multiagent robust consensus problem under model uncertainties, where the uncertain matrices and initial values are bounded by prior intervals. Based on the positive system theory, the related upper and lower dynamic systems are constructed to guarantee that the state value remains within a specified range. Subsequently, in accordance with the Lyapunov stability principle, the observation and consensus errors converge to zero, that is, the real states are reconstructed and consensus is achieved. Both local and neighborhood protocols, which are utilized to realize robust consensus, are presented. Notably, the proposed methods increase the design freedom and eliminate the Metzler constraint on the error matrix by introducing two novel parametric matrices. Without loss of generality, the topology in this article is assumed to contain a directed spanning tree, which can be directly degenerated to the undirected graph. Finally, numerical simulations validating the theoretical results are described.