Robustifying model predictive control of constrained linear systems

Conventional model predictive control implements a version of receding horizon control where uncertainty (in the form of model error, estimation error, or disturbances) is absent. Robustness against uncertainty usually requires minimisation over control policies rather than control sequences; this may be prohibitively difficult in the presence of hard constraints on controls and states. It is shown how robustness may be achieved by a relatively simple modification to any conventional model predictive controller that is guaranteed to be stabilising in the absence of uncertainty