Fast finite-time adaptive neural control of multi-agent systems

This research mainly addresses adaptive neural fast finite-time control of nonlinear strict-feedback multi-agent systems (MASs). Compared to the previous results on finite-time control of multi-agent systems, the nonlinearities in the system under consideration are completely unknown. Furthermore, radial basis function (RBF) neural networks (NNs) are adopted to model these nonlinear functions. In order to avoid the appearance of singularities during derivation, the design of the virtual control functions uses the form of piecewise functions. A novel criterion of fast finite-time stability is developed to solve the proposed control issue. Based on this criterion, a distributed adaptive fast finite-time tracking strategy is presented by combining neural network method and backstepping technique. It is proven that the proposed scheme is able to achieve consensus tracking in finite time. The errors rapidly tend to a small region around the origin, meanwhile other closed-loop signals are always bounded. The effectiveness of the developed control protocol is verified by a numerical simulation.