Robust Steady-State Targets for Model Predictive Control

Abstract

In practice, Model Predictive Control (MPC) algorithms are typically embedded within a multi-level hierarchy of control functions. The MPC algorithm itself is usually implemented in two pieces: a steady-state target calculation followed by a dynamic optimization. We present a new formulation of the steady-state target calculation 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 semi-definite program (SDP) or a second-order cone program (SOCP). A simulation example illustrates the advantages of the proposed algorithm