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

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

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

Access to Document

10.1109/TAC.2015.2495231

Cite this

@article{62bd577f948c4ec1b1008fdbd38f8d19,

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.\}",

note = "Publisher Copyright: {\textcopyright} 1963-2012 IEEE.",

year = "2016",

month = sep,

day = "1",

doi = "10.1109/TAC.2015.2495231",

language = "English",

volume = "61",

pages = "2585--2590",

journal = "IEEE Transactions on Automatic Control",

issn = "0018-9286",

number = "9",

}

TY - JOUR

T1 - Fast Constrained LQR Based on MPC with Linear Decomposition

AU - Nguyen, H. N.

AU - Gutman, P. O.

PY - 2016/9/1

Y1 - 2016/9/1

N2 - 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.

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

U2 - 10.1109/TAC.2015.2495231

DO - 10.1109/TAC.2015.2495231

M3 - Article

AN - SCOPUS:84985961191

SN - 0018-9286

VL - 61

SP - 2585

EP - 2590

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

IS - 9

M1 - 7307989

ER -

Fast Constrained LQR Based on MPC with Linear Decomposition

Abstract

Keywords

Access to Document

Fingerprint

Cite this