An Efficient Dual-Observer Method for Leader-Following Consensus Control of Multiagent Systems

Many existing studies on multi-agent systems (MASs) control have primarily focused on output or consensus tracking problems, often neglecting the impact of external disturbances and the efficient use of control resources. In this paper, we propose a novel adaptive dual-observer control scheme that implements a switched-threshold event-triggered strategy to optimize communication resource utilization. A neural network is leveraged to approximate the nonlinear dynamics of MASs, and the adaptive control strategy is designed to achieve consensus tracking. Then we provide stability analysis to ensure that each agent’s output can effectively follow the leader’s trajectory with a controllable bounded tracking error. Additionally, our scheme is capable to pre-select the lower bound of triggering intervals, which prevents Zeno behaviors, a critical aspect for the practical implementation of controllers. Finally, simulation results on a radar transmitter formation problem validate the effectiveness of our proposed approach.