Parameter optimization of model predictive control by PSO

Ryohei Suzuki, Fukiko Kawai, Chikashi Nakazawa, Tetsuro Matsui, Eitaro Aiyoshi

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Among various control methods, model predictive control (MPC) is one of the major control strategies and has many successful applications. This paper presents an automatic tuning method for MPC using particle swarm optimization (PSO). One of the challenges in MPC is how control parameters can be tuned for various target plants, and the use of PSO for automatic tuning is one of the solutions. The MPC tuning problem is formulated as an optimization problem and PSO is applied as the optimization technique. PSO is one of the metaheuristic methods which are known to seek a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method.

Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Volume178
Issue number1
DOIs
Publication statusPublished - 2012 Jan 15

Fingerprint

Model predictive control
Particle swarm optimization (PSO)
Tuning

Keywords

  • metaheuristics
  • model predictive control
  • parameter optimization
  • particle swarm optimization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Cite this

Parameter optimization of model predictive control by PSO. / Suzuki, Ryohei; Kawai, Fukiko; Nakazawa, Chikashi; Matsui, Tetsuro; Aiyoshi, Eitaro.

In: Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi), Vol. 178, No. 1, 15.01.2012, p. 40-49.

Research output: Contribution to journalArticle

Suzuki, Ryohei ; Kawai, Fukiko ; Nakazawa, Chikashi ; Matsui, Tetsuro ; Aiyoshi, Eitaro. / Parameter optimization of model predictive control by PSO. In: Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi). 2012 ; Vol. 178, No. 1. pp. 40-49.
@article{f4d5f7f5e7754151b5cc209c5b7de6ac,
title = "Parameter optimization of model predictive control by PSO",
abstract = "Among various control methods, model predictive control (MPC) is one of the major control strategies and has many successful applications. This paper presents an automatic tuning method for MPC using particle swarm optimization (PSO). One of the challenges in MPC is how control parameters can be tuned for various target plants, and the use of PSO for automatic tuning is one of the solutions. The MPC tuning problem is formulated as an optimization problem and PSO is applied as the optimization technique. PSO is one of the metaheuristic methods which are known to seek a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method.",
keywords = "metaheuristics, model predictive control, parameter optimization, particle swarm optimization",
author = "Ryohei Suzuki and Fukiko Kawai and Chikashi Nakazawa and Tetsuro Matsui and Eitaro Aiyoshi",
year = "2012",
month = "1",
day = "15",
doi = "10.1002/eej.21188",
language = "English",
volume = "178",
pages = "40--49",
journal = "Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)",
issn = "0424-7760",
publisher = "John Wiley and Sons Inc.",
number = "1",

}

TY - JOUR

T1 - Parameter optimization of model predictive control by PSO

AU - Suzuki, Ryohei

AU - Kawai, Fukiko

AU - Nakazawa, Chikashi

AU - Matsui, Tetsuro

AU - Aiyoshi, Eitaro

PY - 2012/1/15

Y1 - 2012/1/15

N2 - Among various control methods, model predictive control (MPC) is one of the major control strategies and has many successful applications. This paper presents an automatic tuning method for MPC using particle swarm optimization (PSO). One of the challenges in MPC is how control parameters can be tuned for various target plants, and the use of PSO for automatic tuning is one of the solutions. The MPC tuning problem is formulated as an optimization problem and PSO is applied as the optimization technique. PSO is one of the metaheuristic methods which are known to seek a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method.

AB - Among various control methods, model predictive control (MPC) is one of the major control strategies and has many successful applications. This paper presents an automatic tuning method for MPC using particle swarm optimization (PSO). One of the challenges in MPC is how control parameters can be tuned for various target plants, and the use of PSO for automatic tuning is one of the solutions. The MPC tuning problem is formulated as an optimization problem and PSO is applied as the optimization technique. PSO is one of the metaheuristic methods which are known to seek a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method.

KW - metaheuristics

KW - model predictive control

KW - parameter optimization

KW - particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=80053322246&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053322246&partnerID=8YFLogxK

U2 - 10.1002/eej.21188

DO - 10.1002/eej.21188

M3 - Article

VL - 178

SP - 40

EP - 49

JO - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)

JF - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)

SN - 0424-7760

IS - 1

ER -