Robust Stabilization of Nonlinear Processes Using Hybrid Predictive Control

In this work, we consider nonlinear processes with input constraints and uncertain variables, and develop a robust hybrid predictive control structure that provides a safety net for the implementationof model predictive controllers (MPC). The key idea is to use a Lyapunov-based bounded robust controller, for which an explicit characterization of the region of robust closed-loop stability can be obtained, to serve as a backup controller. A supervisor orchestrates switching between MPC and the bounded robust controller in a way that exploits the performance of MPC whenever possible, while using the bounded controller to maintain robust closed--loop stability in the event that the MPC fails yield a control move or leads to instability. The implementation and efficacy of the robust hybridpredictive control structure are demonstrated through simulations using a chemical process example.