Robust Model Predictive Control using Iterative Learning

In this paper we use Iterative Learning Control (ILC) to improve the performance of the Model Predictive Control (MPC) in presence of model mismatch and repetitive disturbances. An MPC is designed for the nominal system using a prediction model including a Kalman estimator. The ILC will be activated when an mismatch happen in the identified model and the real system and/or a disturbance occurs. In this case the repetitive strategy will reduce the error by learning form previous execution. Indeed, it is a design of master and salve control, where the uncertain system is a slave which tries to follow the master dynamics and improve it in each iteration. A model of the inverted dynamics of master system in open loop is used to design the ILC. Finally, the performance of the designed controller is verified using injection molding process.