A general procedure leading to an enhancement of robustness of existing model predictive control techniques is proposed. This procedure, which considers additive modeling errors, is illustrated for the case of cautious stable predictive control. The basic idea is the augmentation of the cost function with an additional term related to a description of the nominal model uncertainty, leading either to a minimization or to a min-max optimization problem, depending on the class of error description being used.