Distributed Online Self‐Tuning Event‐Triggered Adaptive Control With Parameter Uncertainties

This article investigates an online self‐tuning event‐triggered distributed tracking control for heterogeneous multi‐agent systems (HMASs) with multiple parameter uncertainties, which is of great significance in addressing complex real‐world scenarios where system parameters are often unknown and variable. To begin with, considering the scenario where the leader's system matrix is only available to neighboring agents, an adaptive event‐triggered distributed observer is proposed, which estimates the leader's system matrix using a state‐independent power function as the event‐triggered threshold. Building on this, a novel distributed observer based on closed‐loop reference models is designed to estimate the unmeasurable system states. Then, a new adaptive event‐triggered control (ETC) scheme incorporating feedback gain and coupling gain is proposed. This scheme has the advantage of adjusting the uncertain parameters of feedback gain and coupling gain in real time, achieving distributed tracking control under multiple unknown parameters. Besides, a dynamic event‐triggered strategy is proposed to reduce the communication load among neighboring agents. Rigorous mathematical analysis shows that the proposed algorithm not only ensures cooperative tracking of unknown and heterogeneous controlled systems but also effectively precludes Zeno behavior. Finally, numerical simulations are conducted to validate the feasibility of the proposed method.