  Model Predictive Control (MPC) has proven to be a powerful tool for the
control of systems with constraints. Nonetheless, in many applications, a major
challenge arises, that is finding the optimal solution within a single sampling
instant to apply a receding-horizon policy. In such cases, many suboptimal
solutions have been proposed, among which the possibility of "blocking" some
moves a-priori. In this paper, we propose a dynamic approach to move blocking,
to exploit the solution already available at the previous iteration, and we
show not only that such an approach preserves asymptotic stability, but also
that the decrease of performance with respect to the ideal solution can be
theoretically bounded.
