Robust model predictive control using a discrete-time recurrent neural network

Yunpeng Pan; Jun Wang

doi:10.1007/978-3-540-87732-5_97

Robust model predictive control using a discrete-time recurrent neural network

Yunpeng Pan
, Jun Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

13 Citations (Scopus)

Abstract

Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easily implemented using simple hardware. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed approach. © 2008 Springer-Verlag Berlin Heidelberg.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2008
Subtitle of host publication	5th International Symposium on Neural Networks, ISNN 2008, Proceedings
Publisher	Springer Verlag
Pages	883-892
Volume	5263 LNCS
ISBN (Print)	3540877312, 9783540877318
DOIs	https://doi.org/10.1007/978-3-540-87732-5_97
Publication status	Published - 2008
Externally published	Yes
Event	5th International Symposium on Neural Networks, ISNN 2008 - Beijing, China Duration: 24 Sept 2008 → 28 Sept 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5263 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Symposium on Neural Networks, ISNN 2008
Place	China
City	Beijing
Period	24/09/08 → 28/09/08

Research Keywords

Minimax optimization
Recurrent neural network
Robust model predictive control

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10.1007/978-3-540-87732-5_97

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Pan, Y., & Wang, J. (2008). Robust model predictive control using a discrete-time recurrent neural network. In Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Proceedings (Vol. 5263 LNCS, pp. 883-892). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5263 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_97

Pan, Yunpeng ; Wang, Jun. / Robust model predictive control using a discrete-time recurrent neural network. Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. Vol. 5263 LNCS Springer Verlag, 2008. pp. 883-892 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easily implemented using simple hardware. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed approach. {\textcopyright} 2008 Springer-Verlag Berlin Heidelberg.",

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author = "Yunpeng Pan and Jun Wang",

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volume = "5263 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "883--892",

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Pan, Y & Wang, J 2008, Robust model predictive control using a discrete-time recurrent neural network. in Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. vol. 5263 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5263 LNCS, Springer Verlag, pp. 883-892, 5th International Symposium on Neural Networks, ISNN 2008, Beijing, China, 24/09/08. https://doi.org/10.1007/978-3-540-87732-5_97

Robust model predictive control using a discrete-time recurrent neural network. / Pan, Yunpeng; Wang, Jun.
Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. Vol. 5263 LNCS Springer Verlag, 2008. p. 883-892 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5263 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Wang, Jun

PY - 2008

Y1 - 2008

N2 - Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easily implemented using simple hardware. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed approach. © 2008 Springer-Verlag Berlin Heidelberg.

AB - Robust model predictive control (MPC) has been investigated widely in the literature. However, for industrial applications, current robust MPC methods are too complex to employ. In this paper, a discrete-time recurrent neural network model is presented to solve the minimax optimization problem involved in robust MPC. The neural network has global exponential convergence property and can be easily implemented using simple hardware. A numerical example is provided to illustrate the effectiveness and efficiency of the proposed approach. © 2008 Springer-Verlag Berlin Heidelberg.

KW - Minimax optimization

KW - Recurrent neural network

KW - Robust model predictive control

UR - https://www.scopus.com/pages/publications/59149085478

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-59149085478&origin=recordpage

U2 - 10.1007/978-3-540-87732-5_97

DO - 10.1007/978-3-540-87732-5_97

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 3540877312

SN - 9783540877318

VL - 5263 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Neural Networks - ISNN 2008

PB - Springer Verlag

T2 - 5th International Symposium on Neural Networks, ISNN 2008

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ER -

Pan Y, Wang J. Robust model predictive control using a discrete-time recurrent neural network. In Advances in Neural Networks - ISNN 2008: 5th International Symposium on Neural Networks, ISNN 2008, Proceedings. Vol. 5263 LNCS. Springer Verlag. 2008. p. 883-892. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-87732-5_97

Robust model predictive control using a discrete-time recurrent neural network

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