Robust iterative learning control for distributed parameter systems with sensor/actuator networks based on high‐order internal model

Abstract The trajectory tracking problem of distributed parameter systems with sensor/actuator networks is discussed. Notably, unlike traditional iterative learning control (ILC), which typically assumes fixed desired trajectories, the desired trajectories in this work vary iteratively and described in terms of high‐order internal model (HOIM). To address unknown disturbance in the systems, this paper proposes a HOIM‐based robust ILC algorithm. By applying the principle of compressive mapping, it is demonstrated that the output error of the systems is bounded under this algorithm. Finally, the numerical simulation results show that, compared with previous ILC algorithms, the proposed algorithm achieves better tracking performance, thereby validating its effectiveness.