  Model predictive control (MPC) is of increasing interest in applications for
constrained control of multivariable systems. However, one of the major
obstacles to its broader use is the computation time and effort required to
solve a possibly non-convex optimal control problem (OCP) online. This paper
introduces a sensitivity-based warmstarting strategy for systems with nonlinear
dynamics and polyhedral constraints with the goal of reducing the computational
footprint of MPC controllers. It predicts changes in the solution of the
parameterized OCP as the parameter varies, by calculating the semiderivative of
the solution mapping. The main novelty of the paper is that the polyhedrality
of the constraints allows us to avoid imposing any constraint qualification
conditions or strict complementarity assumptions. A numerical study featuring
MPC applied to unmanned aerial vehicles illustrates the proposed approach.
