Model-based event-triggered control for linear plant with threshold variable and model states

This paper studies the robustness of model-based event-triggered control systems with respect to the differences between the plant and model matrices. Two types of event conditions, which involve an additional threshold variable and the norm of model states, are investigated, respectively. The tunable parameters in both the event conditions are designed according to the differences between the plant and model matrices. Also, the uncertainties in the plant matrices are considered, and the asymptotic stability can be guaranteed robustly. Moreover, the relationship between the tunable parameters and the model matrices is revealed. Namely, on the one hand, there exists a range of the tunable parameters such that the closed-loop system is asymptotically stable with model matrices in any compact set. On the other hand, if the differences between the plant and model matrices are small enough, the tunable parameters can be set arbitrarily large. Finally, a numerical example is provided to illustrate the efficiency and feasibility of the obtained results.