Distributed adaptive neural consensus tracking control for a class of nonlinear strict-feedback multi-agent systems

摘要

An adaptive tracking protocol is established for a distributed nonlinear strict-feedback multi-agent systems in this paper. Differing from the existing models, the virtual control coefficients in the model of each follower are unknown functions rather than constants. To construct the protocol, radial basis function (RBF) neural networks (NNs) are used to model the unknown nonlinear functions and adaptive neural backstepping technique is utilized to establish the tracking protocol. Finally, the consensus algorithm can guarantee the consensus tracking errors of the multi-agent system are cooperatively semi-globally uniformly ultimately bounded (CSUUB). At last, some simulation results are included to further demonstrate our results.