  This article investigates the problem of controlling linear time-invariant
systems subject to time-varying and a priori unknown cost functions, state and
input constraints, and exogenous disturbances. We combine the online convex
optimization framework with tools from robust model predictive control to
propose an algorithm that is able to guarantee robust constraint satisfaction.
The performance of the closed loop emerging from application of our framework
is studied in terms of its dynamic regret, which is proven to be bounded
linearly by the variation of the cost functions and the magnitude of the
disturbances. We corroborate our theoretical findings and illustrate
implementational aspects of the proposed algorithm by a numerical case study of
a tracking control problem of an autonomous vehicle.

Systems and Control;Systems and Control;Optimization and Control