  This work is concerned with practical stabilization of nonlinear systems by
means of inf-convolution-based sample-and-hold control. It is a fairly general
stabilization technique based on a generic non-smooth control Lyapunov function
(CLF) and robust to actuator uncertainty, measurement noise, etc. The
stabilization technique itself involves computation of descent directions of
the CLF. It turns out that non-exact realization of this computation leads not
just to a quantitative, but also qualitative obstruction in the sense that the
result of the computation might fail to be a descent direction altogether and
there is also no straightforward way to relate it to a descent direction.
Disturbance, primarily measurement noise, complicate the described issue even
more. This work suggests a modified inf-convolution-based control that is
robust w. r. t. system and measurement noise, as well as computational
uncertainty. The assumptions on the CLF are mild, as, e. g., any piece-wise
smooth function, which often results from a numerical LF/CLF construction,
satisfies them. A computational study with a three-wheel robot with dynamical
steering and throttle under various tolerances w. r. t. computational
uncertainty demonstrates the relevance of the addressed issue and the necessity
of modifying the used stabilization technique. Similar analyses may be extended
to other methods which involve optimization, such as Dini aiming or steepest
descent.
