Fixed-Relative-Switched Threshold Strategies for Consensus Tracking Control of Nonlinear Multiagent Systems

This paper investigates event-triggered consensus tracking in nonlinear semi-strict-feedback multi-agent systems (MASs) involving one leader and multiple followers. We first employ radial basis function neural networks (RBFNNs) and backstepping techniques to approximate the unknown nonlinear dynamics, facilitating the design of dual observers to measure the unknown states and disturbances. Then three adaptive event-triggered control (ETC) schemes are proposed: fixed-threshold, relative-threshold, and switched-threshold configurations, each featuring specialized controller architectures and triggering mechanisms. Through Lyapunov stability analysis, we establish that the follower agents can asymptotically track the reference trajectory of the leader, meanwhile, all error signals remain uniform bounded. Our proposed control strategies effectively prevent Zeno behaviors through stringent exclusion criteria. Finally, an empirical case study is presented, which demonstrates the competitive performance of our control framework in terms of achieving consensus tracking and optimizing the triggering efficiency.