Accelerating Nonlinear Model Predictive Control by Constraint Removal

Abstract

Abstract 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 generally results in nonconvex optimal control problems that are much more difficult to solve than their convex linear counterparts, the added complexity of the nonlinear case only affects the offline part of the proposed method. Since the offline calculations only need to be carried out in a preparatory step, their complexity is not restrictive.