Multi-model Mpc with Output Feedback

In this work, a new formulation is presented for the model predictive control (MPC) of a process system that is represented by a finite set of models, each one corresponding to a different operating point. The general case is considered of systems with stable and integrating outputs in closed-loop with output feedback. For this purpose, the controller is based on a non-minimal order model where the state is built with the measured outputs and the manipulated inputs of the control system. Therefore, the state can be considered as perfectly known and, consequently, there is no need to include a state observer in the control loop. This property of the proposed modeling approach is convenient to extend previous stability results of the closed loop system with robust MPC controllers based on state feedback. The controller proposed here is based on the solution of two optimization problems that are solved sequentially at the same time step. The method is illustrated with a simulated example of the process industry. The rigorous simulation of the control of an adiabatic flash of a multi-component hydrocarbon mixture illustrates the application of the robust controller. The dynamic simulation of this process is performed using EMSO – Environment Model Simulation and Optimization. Finally, a comparison with a linear MPC using a single model is presented.