Adaptive Event-Triggered Output Feedback Consensus Tracking Control of Multi-Agent Systems via K-Filters

Abstract

In this paper, the issue of adaptive event-triggered output feedback consensus tracking dynamic surface control is discussed for nonlinear multi-agent systems (MASs) with unmodeled dynamics. The system states are estimated via K-filters. The unknown nonlinear continuous functions are approximated using radial basis function neural networks (RBFNNs). To lighten the load on communication, an event-triggered control (ETC) method with a relative threshold is developed. By using command filter backstepping technology, a unique adaptive consensus tracking control strategy is presented. Then, through Lyapunov stability analysis, all signals in the closed-loop system can be guaranteed to be semi-globally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon can be avoided. Finally, simulation results validate the effectiveness of the proposed method.