Robust constrained receding-horizon predictive control via bounded data uncertainties
The main objective of this work consists of obtaining a new robust and stable Model Predictive Control (MPC). One widely used technique for improving robustness in MPC consists of the Min-Max optimization, where an analogy can be established with the Bounded Data Uncertainties (BDU) method. The BDU is a regularization technique for least-squares problems by taking into account the uncertainty bounds. So BDU both improves robustness in MPC and offers a guided way of tuning the empirically tuned penalization parameter for the control effort in MPC due to the duality that the parameter coincides with the regularization one in BDU. On the other hand, the stability objective is achieved by the use of terminal constraints, in particular the Constrained Receding-Horizon Predictive Control (CRHPC) algorithm, so the original CRHPC-BDU controller is stated, which presents a better performance from the point of view of robustness and stability than a standard MPC. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.
model predictive control min-max optimization regularized least-squares method robustness to uncertainty stability by constraints