  This paper introduces a new framework for analyzing the stability of
discrete-time model predictive controllers acting on continuous-time systems.
The proposed framework introduces the distinction between discretization time
(used to generate the optimal control problem) and sampling time (used to
implement the controller). The paper not only shows that these two time
constants are independent, but also motivates the benefits of selecting a
sampling time that is smaller than the discretization time. The resulting
approach, hereafter referred to as Hypersampled Model Predictive Control,
overcomes the traditional trade-off between performance and computational
complexity that arises when selecting the sampling time of traditional
discrete-time model predictive controllers.
