Adaptive Neural Consensus Tracking Control of Uncertain Nonlinear Multi-agent Systems with Unknown Output Dead-zone

Abstract

This paper addresses the distributed adaptive concensus problem of multi-agent system with unknown output dead-zone. By combining adaptive neural control technique and dynamic surface error design, a local controller for each follower is constructed. Meanwhile, the Nussbaum-type functions are applied to handle the unknown control gain problems aroused by the output dead-zone nonlinearity. The proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation are conducted to further verify our theoretical results.