Model predictive control of nonlinear singularly perturbed systems: Application to a reactor-separator process network

This work focuses on model predictive control of nonlinear singularly perturbed systems. A composite control system using multirate sampling (i.e., fast sampling of the fast state variables and slow sampling of the slow state variables) and consisting of a "fast" feedback controller that stabilizes the fast dynamics and a model predictive controller that stabilizes the slow dynamics and enforces desired performance objectives in the slow subsystem is designed. Using stability results for nonlinear singularly perturbed systems, sufficient conditions for closed-loop system stability are derived. A nonlinear reactor-separator process network which exhibits two-time-scale behavior is used to demonstrate the controller design.