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This brief focuses on an adaptive bipartite consensus issue for nonlinear multi-agent systems (MASs) over signed digraphs from a new way. A new distributed control algorithm, named finite-time prescribed performance control is designed by applying prescribed performance function (PPF) and a novel first-order filter, which not only guarantees that the bipartite consensus errors converge to a prescribed compact set at a finite time, as well as enables the system to achieve the prescribed performance and fast finite time convergence. Furthermore, the neural networks (NNs) are introduced to deal with the continuous unknown nonlinearity and the effect of non-strict feedback structure that exist in the system, and the dynamic surface control (DSC) mechanism applying a novel first-order filter is used to overcome the “explosion of complexity” problem during a controller design process. Simulation experiment of forced damped pendulums (FDPs) is included to show the feasibility of the theoretical works.
Wang et al. (Thu,) studied this question.