Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Generalized predictive control—Part II. Extensions and interpretations
Automatica (Journal of IFAC)
Stable redesign of predictive control
Automatica (Journal of IFAC)
Optimal, predictive, and adaptive control
Optimal, predictive, and adaptive control
Matrix computations (3rd ed.)
Worst-case formulations of model predictive control for systems with bounded parameters
Automatica (Journal of IFAC)
Robust Solutions to Least-Squares Problems with Uncertain Data
SIAM Journal on Matrix Analysis and Applications
Parameter Estimation in the Presence of Bounded Data Uncertainties
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Applied Mathematics
An Efficient Algorithm for a Bounded Errors-in-Variables Model
SIAM Journal on Matrix Analysis and Applications
Tikhonov Regularization and Total Least Squares
SIAM Journal on Matrix Analysis and Applications
Near-Optimal Parameters for Tikhonov and Other Regularization Methods
SIAM Journal on Scientific Computing
Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
SIAM Journal on Matrix Analysis and Applications
Data Fitting Problems with Bounded Uncertainties in the Data
SIAM Journal on Matrix Analysis and Applications
Multivariable Feedback Control: Analysis and Design
Multivariable Feedback Control: Analysis and Design
Robust RHC method with adaptive DA converter applied to BMI based robotic wheelchair
MACMESE'11 Proceedings of the 13th WSEAS international conference on Mathematical and computational methods in science and engineering
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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.