Novel integrated adaptive bipartite consensus control with intermittent updating and communication

This paper studies the bipartite consensus problem of linear multi-agent systems (MASs) with intermittent updating and communication over undirected graphs. Unlike existing bipartite consensus protocols that employ a fixed and common feedback gain matrix, we propose two types of integrated matrix-form adaptive event-triggered schemes for each agent, which effectively expand the selection range of feedback gains. Based on relative state information among neighboring agents, an integrated adaptive event-triggered (IAET) state feedback protocol is developed, and we prove that MASs not only achieve bipartite consensus but also exclude Zeno behavior. Furthermore, by exploiting discrete relative output information, an observer-based IAET output feedback protocol is designed to address the bipartite consensus problem. Notably, the proposed IAET protocols do not require prior knowledge of the Laplacian matrix associated with the communication graph, making them applicable to large-scale networks. In addition, two numerical examples are presented to validate the effectiveness and superiority of the proposed IAET strategies.