Model Predictive Control with Multiple Constraint Horizons

Abstract: In this work we propose a Model Predictive Control (MPC) formulation that splits constraints in two different types. Motivated by safety considerations, the first type of constraint enforces a control-invariant set, while the second type could represent a less restrictive constraint on the system state. This distinction enables closed-loop sub- optimality results for nonlinear MPC with heterogeneous state constraints (distinct constraints across open loop predicted states), and no terminal elements. Removing the non-invariant constraint recovers the partially constrained case. Beyond its theoretical interest, heterogeneous constrained MPC shows how constraint choices shape the system's closed loop. In the partially constrained case, adjusting the constraint horizon (how many predicted- state constraints are enforced) trades estimation accuracy for computational cost. Our analysis yields first, a sub- optimality upper-bound accounting for distinct constraint sets, their horizons and decay rates, that is tighter for short horizons than prior work. Second, to our knowledge, we give the first lower bound (beyond open-loop cost) on closed-loop sub-optimality. Together these bounds provide a powerful analysis framework, allowing designers to evaluate the effect of horizons in MPC sub-optimality. We demonstrate our results via simulations on nonlinear and linear safety-critical systems.