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Min-max feedback model predictive control for constrained linear systems

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TLDR
The control schemes the authors discuss introduce the notion that feedback is present in the receding-horizon implementation of the control, which leads to improved performance, compared to standard model predictive control, and resolves the feasibility difficulties that arise with the min-max techniques.
Abstract
Min-max feedback formulations of model predictive control are discussed, both in the fixed and variable horizon contexts. The control schemes the authors discuss introduce, in the control optimization, the notion that feedback is present in the receding-horizon implementation of the control. This leads to improved performance, compared to standard model predictive control, and resolves the feasibility difficulties that arise with the min-max techniques that are documented in the literature. The stabilizing properties of the methods are discussed as well as some practical implementation details.

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References
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Journal ArticleDOI

The stability of constrained receding horizon control

TL;DR: An infinite horizon controller that allows incorporation of input and state constraints in a receding horizon feedback strategy is developed and guarantees nominal closed-loop stability for all choices of the tuning parameters in the control law.
Journal ArticleDOI

Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: stability and moving-horizon approximations

TL;DR: In this paper, the authors consider a class of feedback systems arising from the regulation of time-varying discrete-time systems using optimal infinite-horizon and movinghorizon feedback laws, characterized by joint constraints on the state and the control, a general nonlinear cost function and nonlinear equations of motion possessing two special properties.
Journal ArticleDOI

Nonlinear Model Predictive Control: A Tutorial and Survey

TL;DR: A streamlined implementation is presented for constrained linear systems and the formulation is shown to be nominally stabilizing in the presence of constraints provided inconsistent state constraints are relaxed.
Proceedings ArticleDOI

Robust Stability of Constrained Model Predictive Control

TL;DR: In this paper, a new design technique for a robust model predictive controller using an uncertainty description expressed in the time-domain is proposed using a set of Finite Impulse Response (FIR) models, and necessary and sufficient conditions for asymptotic stability are stated.
Journal ArticleDOI

Robust stability analysis of constrained l1‐norm model predictive control

TL;DR: In this paper, sufficient conditions for robust closed-loop stability of a class of dynamic matrix control (DMC) systems are presented, where the l 1 norm is used in the objective function of the on-line optimization, resulting in a linear programming problem.
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