Fast Constrained LQR Based on MPC with Linear Decomposition

H. N. Nguyen; P. O. Gutman

doi:10.1109/TAC.2015.2495231

Fast Constrained LQR Based on MPC with Linear Decomposition

H. N. Nguyen
, P. O. Gutman

IFP Energies nouvelles
Technion - Israel Institute of Technology

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

A technique is presented to solve the linear quadratic optimal control problem with state and control constraints. The control law, called LD-MPC, has both implicit and explicit forms. Compared to conventional model predictive control (MPC), with the same feasible region, the implicit LD-MPC has fewer decision variables, and hence may reduce online computational time. One numerical example with comparison to MPC shows the main benefits of the proposed method.

langue originale	Anglais
Numéro d'article	7307989
Pages (de - à)	2585-2590
Nombre de pages	6
journal	IEEE Transactions on Automatic Control
Volume	61
Numéro de publication	9
Les DOIs	https://doi.org/10.1109/TAC.2015.2495231
état	Publié - 1 sept. 2016
Modification externe	Oui

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10.1109/TAC.2015.2495231

Contient cette citation

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title = "Fast Constrained LQR Based on MPC with Linear Decomposition",

abstract = "A technique is presented to solve the linear quadratic optimal control problem with state and control constraints. The control law, called LD-MPC, has both implicit and explicit forms. Compared to conventional model predictive control (MPC), with the same feasible region, the implicit LD-MPC has fewer decision variables, and hence may reduce online computational time. One numerical example with comparison to MPC shows the main benefits of the proposed method.",

keywords = "Computational complexity, discrete-time systems, predictive control, quadratic programming",

author = "Nguyen, \{H. N.\} and Gutman, \{P. O.\}",

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AB - A technique is presented to solve the linear quadratic optimal control problem with state and control constraints. The control law, called LD-MPC, has both implicit and explicit forms. Compared to conventional model predictive control (MPC), with the same feasible region, the implicit LD-MPC has fewer decision variables, and hence may reduce online computational time. One numerical example with comparison to MPC shows the main benefits of the proposed method.

KW - Computational complexity

KW - discrete-time systems

KW - predictive control

KW - quadratic programming

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Fast Constrained LQR Based on MPC with Linear Decomposition

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