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Adaptive consensus of multi-agent systems with unknown nonlinear dynamics

In this paper, the consensus problem of multiagent systems with non-identical unknown nonlinear dynamics following an unknown and nonlinear leader is studied. By parameterizations of unknown nonlinear dynamics of all agents, decentralized adaptive consensus algorithms are proposed in networks with fixed and switching topologies by incorporating local consensus errors in addition to relative position feedback. Analysis of stability and parameter convergence of the proposed algorithm are conducted based on algebraic graph theory, Lyapunov theory and Riccati inequalities. Finally, examples are provided to validate the theoretical results.