Robust adaptive event‐triggered ILC scheme design for discrete‐time non‐repetitive nonlinear systems

Abstract In this study, a robust adaptive event‐triggered iterative learning control (ET‐ILC) method is developed for discrete‐time non‐repetitive nonlinear systems with both time‐varying system parameters and time‐iteration‐varying non‐parametric uncertainty. The proposed method utilizes an iteration‐varying dead‐zone function to estimate both parametric and non‐parametric uncertainties simultaneously. Moreover, an event‐triggered control mechanism including a mixed triggering strategy is employed to minimize the unnecessary usage of communication resource. This strategy updates the controller only when the triggering condition is met, thereby reducing the frequency of controller actions. The results are also extended to a class of multiple‐input–multiple‐output (MIMO) non‐repetitive nonlinear systems. The designed robust adaptive ET‐ILC algorithm not only guarantees perfect learning convergence in the iteration domain but also contributes to saving the system resources. The effectiveness of the presented method is demonstrated by virtue of the rigorous theoretical analysis and the illustrative simulation examples.