Adaptive Bipartite Tracking Consensus with a Dynamic Event-Triggering Mechanism for Uncertain Nonlinear Multi-agent Systems

This paper investigates adaptive event-triggered bipartite tracking consensus for a class of nonlinear multi-agent systems over a signed directed graph. First, a new distributed controller incorporating adaptive gains is designed to cope with the unknown control coefficients involved in the dynamics of followers. Then, a dynamic event-triggering mechanism whose threshold is adjusted online is proposed to reduce sampling/execution frequencies, thus saving the communication resources. Moreover, a sufficient condition is derived to ensure that the bipartite consensus tracking errors converge asymptotically to zero, based on the M-matrix theory and Lyapunov stability theory. Particularly, a strictly positive lower bound for the inter-execution intervals is guaranteed to preclude Zeno behavior. Finally, a numerical simulation is performed to validate the effectiveness of the proposed control method.