Adaptive iteration learning control with iteration-varying event-triggered mechanism for discrete-time nonlinear systems with random initial states

In this work, a novel adaptive event-trigered iterative learning control (ILC) scheme is designed for discrete-time nonlinear systems with time-varying parameters and random initial states. The reference trajectory can be iteration-invariant or iteration-varying. The control input is updated when the event-triggering condition is satisfied, which reduces the action time of controller and consequently the unnecessary waste of communication resource. An adaptive event-triggering condition parameter is designed to improve the efficiency of the event generator. Moreover, the parameter updating law of adaptive iterative learning controller is designed based on the recursive least squares algorithm. With the proposed adaptive ILC with event-triggered mechanism, the learning convergence along the iteration axis is guaranteed and communication burden is reduced. Finally, rigorous theoretical analysis under Lyapunov theory and illustrative examples are given to demonstrate the efficacy of the proposed method.