A Multi-Model Structure for Model Predictive Control

Model Predictive Control (MPC) is a wide popular control technique that can be applied starting from several model structures. In this paper black box models are considered. In particular it is analysed the sets of regressors that it is better to use in order to obtain the best model for multi step prediction. It is observed that for each prediction a different set of real data output and predicted output are available. Based on this observation a multi-model structure is proposed in order to improve the predictions needed in the computation of the MPC control law. A comparison with a classical one-model structure is discussed. A simulation experiment is presented.