Sampled-Data Iterative Learning Control for Linear Parabolic Distributed Parameter Systems with Event-Triggered Strategy

This paper focuses on the output trajectory tracking problem for linear parabolic distributed parameter systems (DPSs) using sampled-data iterative learning control (ILC) approach. In addition, in order to reduce the number of controller updates, an event-triggered control (ETC) method is introduced, in which the event triggering condition is generated by comparing the output error of the previous iteration batch. The convergence of the output error of the system at the sampling instant is analyzed through a rigorous mathematical proof process, which concludes that the system output error at the sampling instant converges along the direction of the iteration axis. Finally, numerical simulations are given to prove the effectiveness of the proposed method.