Neuroadaptive Prescribed-Time Consensus of Uncertain Nonlinear Multi-Agent Systems

This brief addresses the issue of adaptive prescribed-time consensus control for a class of unknown nonlinear multi-agent systems over an undirected connected topology. The radial basis function (RBF) neural networks (NNs) are applied to approximate the unknown nonlinearities present in the system. By utilizing graph theory and Lyapunov stability theory, we demonstrate that the proposed prescribed-time consensus protocol and adaptive law ensure the boundedness of all closed-loop signals in the system. A noteworthy advantage of the proposed method is the ability to achieve consensus within a predetermined time. Finally, a simulation example of a nonlinear Kuramoto oscillator dynamic system is provided to verify the effectiveness and superiority of the proposed scheme.