Online parameter estimation for MPC model uncertainties based on LSCR approach

The paper discusses a novel probabilistic approach for online parameter estimation of the predictor model used in an MPC (Model Predictive Control) setting in the presence of model uncertainties and external disturbances. Model uncertainty makes it hard to compute an optimal control in general case, because it is needed to take into account all possible values of model parameters. Therefore, it is a good way for optimisation to shrink a set of possible model parameters. The proposed method iteratively estimates model parameters using randomized control strategy and algorithm based on LSCR (Leave-out Sign-dominant Correlation Regions) and computes a new control for the estimated parameters using robust MPC. The theoretical results are demonstrated via a model simulation example with two unknown parameters.