  Control Lyapunov function is a central tool in stabilization. It generalizes
an abstract energy function -- a Lyapunov function -- to the case of controlled
systems. It is a known fact that most control Lyapunov functions are non-smooth
-- so is the case in non-holonomic systems, like wheeled robots and cars.
Frameworks for stabilization using non-smooth control Lyapunov functions exist,
like Dini aiming and steepest descent. This work generalizes the related
results to the stochastic case. As the groundwork, sampled control scheme is
chosen in which control actions are computed at discrete moments in time using
discrete measurements of the system state. In such a setup, special attention
should be paid to the sample-to-sample behavior of the control Lyapunov
function. A particular challenge here is a random noise acting on the system.
The central result of this work is a theorem that states, roughly, that if
there is a, generally non-smooth, control Lyapunov function, the given
stochastic dynamical system can be practically stabilized in the
sample-and-hold mode meaning that the control actions are held constant within
sampling time steps. A particular control method chosen is based on
Moreau-Yosida regularization, in other words, inf-convolution of the control
Lyapunov function, but the overall framework is extendable to further control
schemes. It is assumed that the system noise be bounded almost surely, although
the case of unbounded noise is briefly addressed.
