State-estimation-based economic model predictive control of nonlinear systems

In this work, we focus on a class of nonlinear systems and design an estimator-based economic model predictive control (MPC) system which is capable of optimizing closed-loop performance with respect to general economic considerations taken into account in the construction of the cost function. Working with the class of full-state feedback linearizable nonlinear systems, we use a high-gain observer to estimate the nonlinear system state using output measurements and a Lyapunov-based approach to design an economic MPC system that uses the observer state estimates. We prove, using singular perturbation arguments, that the closed-loop system is practically stable provided the observer gain is sufficiently large. We use a chemical process example to demonstrate the ability of the state-estimation-based economic MPC to achieve process time-varying operation that leads to a superior cost performance metric compared to steady-state operation using the same amount of reactant material.