Event-Triggered Based Data-Driven Predictive Iterative Learning Control

In this paper, a novel event-triggered data-driven predictive iterative learning control (ET-DDPILC) scheme is proposed for a class of discrete-time nonlinear networked control systems (NCSs). Firstly, by using dynamic linearization technique (DLT) to transform the nonlinear NCSs into a virtual linear data model with an introduced unknown pseudo partial derivative (PPD) parameter, we established a data-driven predictive iterative learning control framework. Secondly, in order to reduce the number of ineffective updates during the iterative process, an event-triggered mechanism is designed, and the controller updates data only when the event-triggered condition is satisfied, saving network communication resources. Finally, the monotonic convergence properties of tracking error are explored through a rigorous theoretical analysis, and the effectiveness of the proposed ET-DDPILC algorithm is verified by simulation.