Neural Network‐Observer‐Based ILC of Nonlinear Systems With Event‐Driven Strategy

ABSTRACT Unmeasurable states, random initial conditions, and high computation complexity are three main challenging problems in the adaptive iterative learning control (AILC) of nonlinear repetitive systems with the non‐strict‐feedback form. This paper develops an event‐based AILC tracking control method using the backstepping technique to address the three problems simultaneously. A neural network (NN)‐based learning observer is established to solve unmeasurable states. Furthermore, only one NN is required by transforming all the system nonlinearities to the last state, which further decreases the calculation burden of the observer. As for the random initial conditions, a state tracking approach is developed by designing an attenuated trajectory to remove the limitation of identical initial conditions. To reduce the computation complexity, a tracking differentiator is considered to handle the complexity explosion problem, and the corresponding compensation approach is given to reduce the error influence caused by the tracking differentiator. In addition, a saturated‐threshold event‐driven mechanism is designed to enhance the actuator service time and to save storage space. The convergence analysis is executed, and the effectiveness of the presented approach is demonstrated through an illustrative simulation.