Min-Max optimization is often used for improving robustness in Model Predictive Control (MPC). An analogy to this optimization could be the BDU (Bounded Data Uncertainties) method, which is a regularization technique for least-squares problems that takes into account the uncertainty bounds. Stability of MPC can be achieved by using terminal constraints, such as in the CRHPC (Constrained Receding-Horizon Predictive Control) algorithm. By combining both BDU and CRHPC methods, a robust and stable MPC is obtained, which is the aim of this work. BDU also offers a guided method of tuning the empirically tuned penalization parameter for the control effort in MPC. © 2008 Elsevier Inc. All rights reserved.
Ramos, C., Martínez, M., Sanchis, J., & Herrero, J. M. (2008). Robust and stable predictive control with bounded uncertainties. Journal of Mathematical Analysis and Applications, 342(2), 1003–1014. https://doi.org/10.1016/j.jmaa.2007.12.073