A Novel Neural Network Adaptive Approach to Asymptotical Consensus of Uncertain Nonlinear Multiagent Systems With Directed Topology

Although many neural network (NN) adaptive controllers have been proposed to deal with cooperation of nonlinear multiagent systems (MASs), it is still unknown how to achieve asymptotical cooperative goals over a general directed topology. A main challenge is the coupling of nonlinearities learning and cooperative control. Within this context, a novel class of adaptive controllers based on an NN-based cooperative modified state observer (CMSO) is proposed, where the CMSO can approximate unknown nonlinearities so that nonlinearities learning is decoupled into local tracking control under the proposed framework. It is proven that the controllers can achieve asymptotic consensus if the topology has a directed spanning tree. Note that both nonsmooth controllers and smooth controllers are proposed, where smooth controllers can avoid chattering, which may be induced by nonsmooth ones. Finally, a simulation over multiple-robot systems is given to validate the theoretical results.