CONICET | Buscador de Institutos y Recursos Humanos

In this work, it is proposed a model predictive controller (MPC), which is robustly stable for some classes of model uncertainty and to unknown disturbances. It is considered the case of open loop stable systems where only the inputs and controlled outputs are measured. It is assumed that the controller will work in a scenario where target tracking is also required. Here, it is extended the nominal infinite horizon MPC with output feedback. The method considers an extended cost function that can be made globally convergent for any finite input horizon considered for the uncertain system. The approach is based on the explicit inclusion of cost contracting constraints in the control problem. The controller considers the output feedback case through a non-minimal state space model that is built using the past output measurements and the past input increments. The application of the robust output feedback MPC is illustrated through the simulation of a low order multivariable system.