  Digital controller design for nonlinear systems may be complicated by the
fact that an exact discrete-time plant model is not known. One existing
approach employs approximate discrete-time models for stability analysis and
control design, and ensures different types of closedloop stability properties
based on the approximate model and on specific bounds on the mismatch between
the exact and approximate models. Although existing conditions for practical
stability exist, some of which consider the presence of process disturbances,
input-to-state stability with respect to state-measurement errors and based on
approximate discretetime models has not been addressed. In this paper, we thus
extend existing results in two main directions: (a) we provide input-to-state
stability (ISS)-related results where the input is the state measurement error
and (b) our results allow for some specific varying-sampling-rate scenarios. We
provide conditions to ensure semiglobal practical ISS, even under some specific
forms of varying sampling rate. These conditions employ Lyapunov-like
functions. We illustrate the application of our results on numerical examples,
where we show that a bounded state-measurement error can cause a semiglobal
practically stable system to diverge.
