Data-Driven $H_{\infty}$ Consensus Controller for Agents Under Disturbances and Model Uncertainty

We aim to study the application of $H_{\infty}$ consensus controller in the cooperative control of agents with disturbance, design a data-driven control algorithm for the scenario with unknown system model. Firstly, for the consensus problem of high-order linear multi-agent systems with disturbances, a distributed $H_{\infty}$ consensus control algorithm is proposed, which could not only ensure the consensus of multi-agent systems but also effectively improve the anti-disturbance ability of the system. Secondly, aiming at the consensus control problem of agent with disturbance and unknown model, a distributed consensus control algorithm based on data-driven approach is proposed, and it is combined with $H_{\infty}$ consensus controller, which could design appropriate control strategy by collecting agent information in real time. Finally the simulation results show that the Data-Driven $H_{\infty}$ consensus Controller can effectively deal with the challenges caused by disturbances and model uncertainty, and achieve the stability and consensus of the agent consensus.