Event-Triggered Sampled-Data Iterative Learning Control for Linear Time-Varying Switched Systems

Consider a class of continues linear time-varying switched systems and study its sampled-data iterative learning control output tracking problem under event triggering. Firstly, in order to reduce the update batch of the control input in the direction of the iteration axis, an event-triggered sampled-data iterative learning control law is designed and the event-triggered conditions are derived by the Lyapunov function method. Secondly, a fixed threshold method is proposed to accelerate the convergence of the system. Then, according to the activation intervals of the switched subsystem,triggered and untriggered are analyzed for convergence, and the sufficient conditions for consistent convergence of the system are derived by the compression mapping method. Finally, the effectiveness of the algorithm is verified by the numerical simulation.