Practically Predefined-Time Adaptive Consensus Control for Nonlinear Multiagent Systems

This paper presents a novel predefined-time consensus control scheme for multiagent systems (MASs) subject to uncertain nonlinear dynamics. The proposed methodology employs radial basis function neural networks (RBF NNs) to approximate unknown system nonlinearities, ensuring effective compensation of modeling uncertainties. A key contribution lies in the development of a nonsingular predefined-time control law, synthesized through the incorporation of a continuously differentiable smooth function, which effectively circumvents potential singularity issues. Furthermore, to alleviate "explosion of complexity" issue associated with conventional backstepping approaches, a predefined-time dynamic surface filter is introduced. Lyapunov-Based stability analysis rigorously establishes that all closed-loop signals remain uniformly ultimately bounded within a predefined time. Simulation experiment further validates the effectiveness and superiority of the proposed predefined-time control algorithm