Our group aims to build a control framework in which learning-based
components such as vision-language models and physics-informed
prediction models are combined with control design, where stochastic
control theory is responsible for suppressing the effect of plant
randomness whose realization is not available to those components in
advance. This talk begins with an informal picture of this direction
and a brief review of stabilization theory for discrete-time systems
whose dynamics are determined by a general stochastic process. The
main part is then devoted to a specific class within this setting,
descriptor systems described by random polytopes, for which
gain-scheduled state feedback controllers can be designed via linear
matrix inequalities. This part is based on recent joint work with Dr.
Dimitri Peaucelle and colleagues, which was presented at the IFAC
World Congress 2026.