In the paper, a nonlinear model predictive control (NMPC) formulation with aggregated constraints approach is introduced. Constraint aggregation function lump the original constraints of the control problem into a reduced set of nonlinear constraints. Then significant saving in the computational footprint of solving the MNPC can be achieved. The effect of the aggregation on the closed-loop system performance and stability is investigated with the use of sensitivity analysis tools. Finally, general constructions are illustrated with a model example.