Event‐Triggered Data‐Driven Error‐Tracking Learning Control: Theory and Experiments

ABSTRACT In this work, an event‐triggered data‐driven error‐tracking learning control method is developed for a class of discrete‐time systems with unknown time‐varying parameters and nonrepetitive initial conditions. First, an error‐tracking strategy is designed for discrete‐time iterative learning control systems to relax the strict initial condition constraint required in most existing literature. This approach achieves accurate tracking of the system output error to a predetermined reference error trajectory using only input/output data in the presence of initial state shifts. Furthermore, a time‐iteration direction event‐triggered mechanism is designed to ensure that the update frequency of the error‐tracking learning controller and parameter estimator is reduced without compromising the control accuracy. Theoretical analysis confirms the convergence of tracking errors, and numerical simulations validate the feasibility of the proposed scheme. Experimental results on the Universal Robot 5 manipulator further validate the effectiveness and practicality of the proposed method.