  This paper introduces a computationally efficient approach for solving Model
Predictive Control (MPC) reference tracking problems with state and control
constraints. The approach consists of three key components: First, a log-domain
interior-point quadratic programming method that forms the basis of the overall
approach; second, a method of warm-starting this optimizer by using the MPC
solution from the previous timestep; and third, a computational governor that
bounds the suboptimality of the warm-start by altering the reference command
provided to the MPC problem. As a result, the closed-loop system is altered in
a manner so that MPC solutions can be computed using fewer optimizer iterations
per timestep. In a numerical experiment, the computational governor reduces the
worst-case computation time of a standard MPC implementation by 90, while
maintaining good closed-loop performance.
