Distributed data-driven iterative learning control for multi-agent systems with unknown input-output coupled parameters

This article studies a distributed data-driven iterative learning control (ILC) strategy based on the identified input–output coupled parameters (IOCPs) to address the consensus trajectory tracking problem of discrete time-varying multi-agent systems (MASs). First, by leveraging the repeatability of the control system, a special learning scheme is designed by using system input and output data to identify the unknown IOCPs. Then the reciprocal of the identified IOCPs is selected as the learning gain to construct the ILC law of the MASs. Second, the case of measurement noise in the MASs is considered, where the maximum allowable control deviation is incorporated into the learning mechanism for identification of the IOCPs, thereby minimizing adverse effects of the noise on the learning scheme's performance and bolstering robustness. Finally, three numerical simulations are employed to validate the effectiveness of the designed IOCP identification method and iterative learning control strategy.