Adaptive Neural Bipartite Containment of Deferred State-Constrained Multi-Agent Systems Under Event-Triggered Communication

The issue of adaptive neural bipartite containment of multi-agent systems (MASs) with deferred state constraints under event-triggered communication (ETC) is explored in this article. Firstly, a dynamic ETC mechanism that only transmits local leader reference signals among follower agents is proposed, effectively reducing communication overhead. Subsequently, state constraints are addressed under entirely unknown initial states by employing a shift function and a nonlinear state-dependent function. Finally, an adaptive neural bipartite containment controller is designed to decrease computational burden, with each controller incorporating a single adaptive law, and the proposed virtual and actual controllers do not involve feasibility conditions and partial derivative terms. It is demonstrated that all closed-loop signals remain bounded, the bipartite containment error converges to a user-defined interval, and state constraints are not breached. The efficacy of the proposed scheme is further verified through two simulation examples.