Fast Finite-Time Neuroadaptive Consensus Control for Nonlinear Nontriangular Structured Multiagent Systems With Uncertainty

This literature is concerned with fast finite-time adaptive consensus control for nonlinear nontriangular structured (NTS) multiagent systems (MASs) with uncertainty. In contrast to the existing finite-time schemes, the consensus control for NTS MASs has two design difficulties, reflected in the nonaffine characteristics of the control input and the algebraic loop from the direct backstepping. Moreover, due to the repeated utilization of necessary inequalities, the traditional adaptive framework is incapable of achieving the desired finite-time asymptotic consensus and only rests on satisfying the practical finite-time stability. To tackle such obstacles, a novel adaptive neural algorithm is discussed via the modified tuning function as well as the projection operator in this work. Under the fast finite-time stabilizer constructed, the error of the NTS MASs converges asymptotically to a preset range within the finite time. The flexibility of the algorithm is demonstrated by the simulation example.