In practice, model predictive control (MPC) algorithms are typically embedded within a multilevel hierarchy of control functions. The MPC algorithm itself is usually implemented in two pieces: a steady-state target calculation followed by a dynamic optimization. A new formulation of the steady-state target calculation is presented that explicitly accounts for model uncertainty. When model uncertainty is incorporated, the linear program associated with the steady-state target calculation can be recast as a second-order cone program. This article shows how primal-dual interior-point methods can take advantage of the resulting structure. Simulation examples illustrate the effect of uncertainty on the steady-state target calculation and demonstrate the advantages of interior-point methods.