A population extremal optimization based modified constrained generalized predictive control method

Hai-Yang LIU; Kang-Di LU; Guo-Qiang ZENG; Huan WANG; Yu-Xing DAI

doi:10.1109/CCDC.2017.7978641

A population extremal optimization based modified constrained generalized predictive control method

Hai-Yang LIU
, Kang-Di LU
, Guo-Qiang ZENG^*
, Huan WANG
, Yu-Xing DAI

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

As one of the most popular and successful methods in industrial applications, model predictive control (MPC) has attracted increasing interest in the past two decades. However, one of open issues in this research filed is how to solve the constrained nonlinear optimization problems in MPC. From the perspective of evolutionary algorithm, this paper presents a novel population extremal optimization (PEO) based modified constrained generalized predictive control (CGPC) method called CGPC-PEO. The key idea behind the proposed CGPC-PEO is using PEO for rolling optimization to minimize the weighted objective function subjecting to a set of constraints. Its superiority to other evolutionary algorithms such as genetic algorithm and particle swarm optimization based CGPC is demonstrated by the simulation results on an industrial process plant.

Original language	English
Title of host publication	Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017
Publisher	IEEE
Pages	863-869
Number of pages	7
ISBN (Electronic)	9781509046560
ISBN (Print)	9781509046584
DOIs	https://doi.org/10.1109/CCDC.2017.7978641
Publication status	Published - 2017
Externally published	Yes
Event	29th Chinese Control And Decision Conference, CCDC 2017 - Chongqing, China Duration: 28 May 2017 → 30 May 2017

Conference

Conference	29th Chinese Control And Decision Conference, CCDC 2017
Country/Territory	China
City	Chongqing
Period	28/05/17 → 30/05/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Funding

This work is supported by Zhejiang Provincial Natural Science Foundation of China under Grants LY16F030011, LZ16E050002, LQ14F030006, and LQ14F030007, Zhejiang Province Science and Technology Planning Project under Grants 2014C31074, 2014C31093, and 2015C31157, National Nature Science Foundation under Grant 51207112.

Keywords

Constrained Generalized Predictive Control
Evolutionary Algorithms
Population Extremal Optimization
Rolling Optimization

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10.1109/CCDC.2017.7978641

Cite this

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title = "A population extremal optimization based modified constrained generalized predictive control method",

abstract = "As one of the most popular and successful methods in industrial applications, model predictive control (MPC) has attracted increasing interest in the past two decades. However, one of open issues in this research filed is how to solve the constrained nonlinear optimization problems in MPC. From the perspective of evolutionary algorithm, this paper presents a novel population extremal optimization (PEO) based modified constrained generalized predictive control (CGPC) method called CGPC-PEO. The key idea behind the proposed CGPC-PEO is using PEO for rolling optimization to minimize the weighted objective function subjecting to a set of constraints. Its superiority to other evolutionary algorithms such as genetic algorithm and particle swarm optimization based CGPC is demonstrated by the simulation results on an industrial process plant.",

keywords = "Constrained Generalized Predictive Control, Evolutionary Algorithms, Population Extremal Optimization, Rolling Optimization",

author = "Hai-Yang LIU and Kang-Di LU and Guo-Qiang ZENG and Huan WANG and Yu-Xing DAI",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 29th Chinese Control And Decision Conference, CCDC 2017 ; Conference date: 28-05-2017 Through 30-05-2017",

year = "2017",

doi = "10.1109/CCDC.2017.7978641",

language = "English",

isbn = "9781509046584",

pages = "863--869",

booktitle = "Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017",

publisher = "IEEE",

}

LIU, H-Y, LU, K-D, ZENG, G-Q, WANG, H & DAI, Y-X 2017, A population extremal optimization based modified constrained generalized predictive control method. in Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017., 7978641, IEEE, pp. 863-869, 29th Chinese Control And Decision Conference, CCDC 2017, Chongqing, China, 28/05/17. https://doi.org/10.1109/CCDC.2017.7978641

A population extremal optimization based modified constrained generalized predictive control method. / LIU, Hai-Yang; LU, Kang-Di; ZENG, Guo-Qiang et al.
Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017. IEEE, 2017. p. 863-869 7978641.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - A population extremal optimization based modified constrained generalized predictive control method

AU - LIU, Hai-Yang

AU - LU, Kang-Di

AU - ZENG, Guo-Qiang

AU - WANG, Huan

AU - DAI, Yu-Xing

PY - 2017

Y1 - 2017

N2 - As one of the most popular and successful methods in industrial applications, model predictive control (MPC) has attracted increasing interest in the past two decades. However, one of open issues in this research filed is how to solve the constrained nonlinear optimization problems in MPC. From the perspective of evolutionary algorithm, this paper presents a novel population extremal optimization (PEO) based modified constrained generalized predictive control (CGPC) method called CGPC-PEO. The key idea behind the proposed CGPC-PEO is using PEO for rolling optimization to minimize the weighted objective function subjecting to a set of constraints. Its superiority to other evolutionary algorithms such as genetic algorithm and particle swarm optimization based CGPC is demonstrated by the simulation results on an industrial process plant.

AB - As one of the most popular and successful methods in industrial applications, model predictive control (MPC) has attracted increasing interest in the past two decades. However, one of open issues in this research filed is how to solve the constrained nonlinear optimization problems in MPC. From the perspective of evolutionary algorithm, this paper presents a novel population extremal optimization (PEO) based modified constrained generalized predictive control (CGPC) method called CGPC-PEO. The key idea behind the proposed CGPC-PEO is using PEO for rolling optimization to minimize the weighted objective function subjecting to a set of constraints. Its superiority to other evolutionary algorithms such as genetic algorithm and particle swarm optimization based CGPC is demonstrated by the simulation results on an industrial process plant.

KW - Constrained Generalized Predictive Control

KW - Evolutionary Algorithms

KW - Population Extremal Optimization

KW - Rolling Optimization

UR - https://www.scopus.com/pages/publications/85028036250

U2 - 10.1109/CCDC.2017.7978641

DO - 10.1109/CCDC.2017.7978641

M3 - Conference paper (refereed)

AN - SCOPUS:85028036250

SN - 9781509046584

SP - 863

EP - 869

BT - Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

PB - IEEE

T2 - 29th Chinese Control And Decision Conference, CCDC 2017

Y2 - 28 May 2017 through 30 May 2017

ER -

A population extremal optimization based modified constrained generalized predictive control method

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Keywords

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