How could non-feasible constraints be located in predictive control?

Model Predictive Control (MPC) is one of the most popular advanced control techniques. One of the reasons is that it takes into account the process constraints in a natural way. But there are situations (perturbations, not well defined constraints, etc.) when it is not possible to compute a sequence of future controls because some constraints are violated, i.e., the problem is not feasible. When this sort of problem appears, it is necessary to apply some infeasibility handling procedure that solves the problem. Mainly there are two ways: removing constraints or relaxing the limits. But it is necessary to know which constraints are responsible for the infeasibility. There are optimization procedures that have been specifically developed to locate these problematic limits. These techniques will be applied to the control problem and compared with the approaches found in MPC papers.