Design of stabilizing output feedback nonlinear model predictive controllers with an application to DC-DC converters

This paper focuses on the synthesis of nonlinear Model Predictive Controllers that can guarantee robustness with respect to measurement noise. The input-to-state stability framework is employed to analyze the robustness of the resulting Model Predictive Control (MPC) closed-loop system. It is illustrated how the obtained robustness result can be employed to synthesize asymptotically stabilizing observer-based output-feedback nonlinear MPC controllers for a class of nonlinear discrete-time systems. The developed theory is illustrated by applying it to control a Buck-Boost DC-DC converter.