We accelerate nonlinear model predictive control with an approach that successively detects
and removes inactive constraints from the optimal control problem. In every time step and for
every constraint, the cost function value is compared to a bound that can be calculated
offline. If the current cost function value drops below one of these bounds, the corresponding
constraint can be removed. We show how to extend this constraint removal method, which
was originally developed for linear MPC, to the nonlinear case. While nonlinear MPC …

Abstract Model predictive control (MPC) is computationally expensive, because it is based
on solving an optimal control problem in every time step. We show how to reduce the
computational cost of linear discrete-time MPC by detecting and removing inactive
constraints from the optimal control problem. State of the art MPC implementations detect
constraints that are inactive for all times and all initial conditions and remove these from the
underlying optimization problem. Our approach, in contrast, detects constraints that become …