Robust MPC with Output Feedback and Realigned Model

In this work, it is presented a contribution to the design of the robust MPC with output feedback and input constraints. This work extends existing approaches by considering a particular non-minimal state space model, which transforms the output feedback strategy into a state feedback strategy. The controller is developed to the case in which the system inputs may become saturated. We follow a two stages approach. In the off-line stage, a series of unconstrained robust MPCs is obtained by including in the control optimization problem, inequality constraints that force the state of the closed-loop system to contract along the time. Each of these controllers is associated to particular sets of manipulated inputs and controlled outputs. In the existing version of the method, the closed loop system involves a state observer that makes the solution to the robust MPC optimization problem a time consuming step. In the on-line step of the controller design proposed procedure, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. The method is illustrated with a simulation example of the process industry. Keyword: Model Predictive Control, Robust stability, Output feedback, Input constraints.