Integration of real-time optimization and model predictive control

This paper proposes a controller design approach that integrates RTO and MPC for the control of constrained uncertain nonlinear systems. Assuming that the economic function is a known function of constrained system’s states, parameterized by unknown parameters and time-varying, the controller design objective is to simultaneously identify and regulate the system to the optimal operating point. The approach relies on a novel set-based parameter estimation routine and a robust model predictive controller that takes into the effect of parameter estimation errors. A simulation example is used to demonstrate the effectiveness of the design technique.