Event‐triggered output feedback predictive control for Takagi‐Sugeno model with bounded disturbance and redundant channels

Abstract In this paper, the problem of dynamic output feedback model predictive control (MPC) for the Takagi‐Sugeno (T‐S) model with bounded disturbance and redundant channels is investigated based on an event‐triggered scheme. The redundant channels, described by a set of mutually independent Bernoulli processes, are applied to improve the reliability of data transmission. The state immeasurable is considered, and an event‐triggered state estimator with a time‐varying threshold is designed to estimate the true system state. The estimator and controller gains are parameterized as a non‐convex optimization problem. In order to online solve the resulting optimization problem, the cone complementarity approach (CCA) with iteration optimization is adopted. The mean‐square quadratic boundedness (MSQB) is utilized to characterize the invariance of the system state and guarantee the closed‐loop stability, while the recursive feasibility is ensured via online refreshing the estimation error set. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed theoretical method.