Fast data-driven iterative event-triggered control for nonlinear networked discrete systems with data dropouts and sensor saturation

Abstract

The tracking control problem is investigated for a class of nonlinear networked discrete systems with random data-dropout and sensor saturation, and a novel data-driven iterative learning event-triggered control scheme is proposed. First, a new model is established to describe random data-dropout processes. Then, the fixed threshold iterative learning control scheme based on randomly received saturated output data is designed to track the desired trajectory and reduce the update number of iterations. Further, in order to obtain a faster convergence or learning speed, a novel method based on varying parameter along iteration axis is given. In the end, the resulting closed-loop system is proved to be stable and the relationship between the upper bound of the consecutive data-dropout number and system stability is revealed. Complete simulations are exploited to verify theoretical results.