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

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

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.

Original language	English
Article number	7307989
Pages (from-to)	2585-2590
Number of pages	6
Journal	IEEE Transactions on Automatic Control
Volume	61
Issue number	9
DOIs	https://doi.org/10.1109/TAC.2015.2495231
Publication status	Published - 1 Sept 2016
Externally published	Yes

Keywords

Computational complexity
discrete-time systems
predictive control
quadratic programming

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10.1109/TAC.2015.2495231

Cite this

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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.",

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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

Abstract

Keywords

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