  In this paper we propose an Alternating Direction Method of Multipliers
(ADMM) algorithm for solving a Model Predictive Control (MPC) optimization
problem, in which the system has state and input constraints and a nonlinear
input map. The resulting optimization is nonconvex, and we provide a proof of
convergence to a point satisfying necessary conditions for optimality. This
general method is proposed as a solution for blended mode control of hybrid
electric vehicles, to allow optimization in real time. To demonstrate the
properties of the algorithm we conduct numerical experiments on randomly
generated problems, and show that the algorithm is effective for achieving an
approximate solution, but has limitations when an exact solution is required.
