Dual-Domain Triggered Iterative Learning Control for Networked Switched Systems Against Denial-of-Service Attacks

In this article, dual-domain triggered iterative learning control (ILC) is studied for networked switched systems against denial-of-service (DoS) attacks. Unlike existing research on ILC focusing only on updating between iterations, a novel dual-domain (time and iteration domains) triggered ILC is proposed. The updates between iterations and updates of network feedback information for each iteration on the time scale are performed, reducing the iteration steps and network data transmission frequency within each iteration. In each iteration, the system output is transmitted via a network, which is assumed to be vulnerable to DoS attacks. An attack detection mechanism and buffer-based compensation mechanism are proposed. Then, the boundedness of the iterative tracking error of the networked switched systems is ensured through a switching stability analysis based on the Lyapunov theory. Finally, the advantages of the proposed methods are substantiated by a simulation example consisting of three parts.