A Continuous Neural Network Adaptive Controller for Consensus of Uncertain Multi-Agent Systems
Peijun Wang, Huanhuan Tian, Di Huang, Tingwen Huang
Abstract
We investigate the consensus problems for uncertain multi-agent systems (MASs) via a neural network (NN) adaptive control approach. However, it is quite difficult because of the existence of NN approximation errors. Unlike existing works that use discontinuous functions to eliminate the effects of NN approximation errors, a novel continuous function is proposed by utilizing the idea of time varying boundary layer technique. Firstly, we design a continuous NN adaptive controller with dynamic coupling strengths for the case with time invariant topology. It is shown that asymptotical consensus can be achieved without requiring any global information. Secondly, we consider time varying communication networks and design a continuous NN adaptive controller with fixed coupling strength. It is shown that the consensus error is ultimately uniformly bounded if the average dwell time is larger than a positive scalar. Finally, two examples are studied to verify the theoretical results, respectively.