期刊
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS
卷 342, 期 2, 页码 1003-1014出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmaa.2007.12.073
关键词
model predictive control; min-max optimization; regularization; robustness; stability
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. (C) 2008 Elsevier Inc. All rights reserved.
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