Dynamic Event‐Triggered Data‐Driven Indirect Iterative Learning Control for Repetitive Nonlinear Systems Under Mixed Attacks

ABSTRACT This article researches the dynamic event‐triggered indirect iterative learning control for repetitive nonlinear systems subject to mixed attacks. First, two control loops are designed to enhance tracking precision. More specifically, an adaptive set‐point adjustment mechanism is developed within the outer loop to dynamically modify the virtual output values. A novel compensation mechanism along the iteration axis is presented within the inner loop, which utilizes historical data to replace the lost variables. Second, the nonlinear model is converted into a data‐related linear model via a proposed dual dynamic linearization technique. Based on the virtual output and the last trigger value, a new dynamic event‐triggered mechanism is devised to optimize the information exchange frequency in the outer loop. The proposed control scheme achieves tracking of the target trajectory under mixed attacks. Finally, two case studies demonstrate the effectiveness of the developed control strategy.