Parameter optimization of model predictive control using PSO

Ryohei Susuki; Fukiko Kawai; Chikashi Nakazawa; Tetsuro Matsui; Eitaro Aiyoshi

doi:10.1109/SICE.2008.4654987

Parameter optimization of model predictive control using PSO

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

Department of Applied Physics and Physico-Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Citations (Scopus)

Abstract

Among various control methods, model predictive control (MPC) becomes one of the major control strategies and has many successful applications. This paper presents an automatic tuning method of MPC using particle swarm optimization (PSO). One of the challenges in MPC is how the control parameters can be tuned for various target plants, and usage of PSO for automatic tuning is one of the solutions. The tuning problem of MPC is formulated as an optimization problem and PSO is applied as the optimization techniques. PSO is one of meta-heuristic methods which are known to search 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 language	English
Title of host publication	Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
Pages	1981-1988
Number of pages	8
DOIs	https://doi.org/10.1109/SICE.2008.4654987
Publication status	Published - 2008 Dec 1
Event	SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan Duration: 2008 Aug 20 → 2008 Aug 22

Publication series

Name	Proceedings of the SICE Annual Conference

Other

Other	SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
Country/Territory	Japan
City	Tokyo
Period	08/8/20 → 08/8/22

Keywords

Feed back system
Model predictive control
Particle swarm optimizaiton

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/SICE.2008.4654987

Cite this

Susuki, R., Kawai, F., Nakazawa, C., Matsui, T., & Aiyoshi, E. (2008). Parameter optimization of model predictive control using PSO. In Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology (pp. 1981-1988). [4654987] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2008.4654987

Parameter optimization of model predictive control using PSO. / Susuki, Ryohei; Kawai, Fukiko; Nakazawa, Chikashi et al.
Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology. 2008. p. 1981-1988 4654987 (Proceedings of the SICE Annual Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Susuki, R, Kawai, F, Nakazawa, C, Matsui, T & Aiyoshi, E 2008, Parameter optimization of model predictive control using PSO. in Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology., 4654987, Proceedings of the SICE Annual Conference, pp. 1981-1988, SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology, Tokyo, Japan, 08/8/20. https://doi.org/10.1109/SICE.2008.4654987

@inproceedings{282f99c4b91e4074835dff2bdf27856f,

title = "Parameter optimization of model predictive control using PSO",

abstract = "Among various control methods, model predictive control (MPC) becomes one of the major control strategies and has many successful applications. This paper presents an automatic tuning method of MPC using particle swarm optimization (PSO). One of the challenges in MPC is how the control parameters can be tuned for various target plants, and usage of PSO for automatic tuning is one of the solutions. The tuning problem of MPC is formulated as an optimization problem and PSO is applied as the optimization techniques. PSO is one of meta-heuristic methods which are known to search 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 = "Feed back system, Model predictive control, Particle swarm optimizaiton",

author = "Ryohei Susuki and Fukiko Kawai and Chikashi Nakazawa and Tetsuro Matsui and Eitaro Aiyoshi",

year = "2008",

month = dec,

day = "1",

doi = "10.1109/SICE.2008.4654987",

language = "English",

isbn = "9784907764296",

series = "Proceedings of the SICE Annual Conference",

pages = "1981--1988",

booktitle = "Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology",

note = "SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology ; Conference date: 20-08-2008 Through 22-08-2008",

}

TY - GEN

T1 - Parameter optimization of model predictive control using PSO

AU - Susuki, Ryohei

AU - Kawai, Fukiko

AU - Nakazawa, Chikashi

AU - Matsui, Tetsuro

AU - Aiyoshi, Eitaro

PY - 2008/12/1

Y1 - 2008/12/1

N2 - Among various control methods, model predictive control (MPC) becomes one of the major control strategies and has many successful applications. This paper presents an automatic tuning method of MPC using particle swarm optimization (PSO). One of the challenges in MPC is how the control parameters can be tuned for various target plants, and usage of PSO for automatic tuning is one of the solutions. The tuning problem of MPC is formulated as an optimization problem and PSO is applied as the optimization techniques. PSO is one of meta-heuristic methods which are known to search 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) becomes one of the major control strategies and has many successful applications. This paper presents an automatic tuning method of MPC using particle swarm optimization (PSO). One of the challenges in MPC is how the control parameters can be tuned for various target plants, and usage of PSO for automatic tuning is one of the solutions. The tuning problem of MPC is formulated as an optimization problem and PSO is applied as the optimization techniques. PSO is one of meta-heuristic methods which are known to search 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 - Feed back system

KW - Model predictive control

KW - Particle swarm optimizaiton

UR - http://www.scopus.com/inward/record.url?scp=56749158989&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=56749158989&partnerID=8YFLogxK

U2 - 10.1109/SICE.2008.4654987

DO - 10.1109/SICE.2008.4654987

M3 - Conference contribution

AN - SCOPUS:56749158989

SN - 9784907764296

T3 - Proceedings of the SICE Annual Conference

SP - 1981

EP - 1988

BT - Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology

T2 - SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology

Y2 - 20 August 2008 through 22 August 2008

ER -

Parameter optimization of model predictive control using PSO

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this