Model predictive control of linear parameter varying systems based on a recurrent neural network

Zheng Yan; Xinyi Le; Jun Wang

doi:10.1007/978-3-319-13749-0_22

Model predictive control of linear parameter varying systems based on a recurrent neural network

Zheng Yan
, Xinyi Le
, Jun Wang

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

Abstract

This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach. © Springer International Publishing Switzerland 2014

Original language	English
Title of host publication	Theory and Practice of Natural Computing
Subtitle of host publication	Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings
Editors	Adrian-Horia Dediu, Manuel Lozano, Carlos Martín-Vide
Place of Publication	Cham
Publisher	Springer
Pages	255-266
ISBN (Electronic)	978-3-319-13749-0
ISBN (Print)	9783319137483
DOIs	https://doi.org/10.1007/978-3-319-13749-0_22
Publication status	Published - 2014
Externally published	Yes
Event	3rd International Conference on the Theory and Practice of Natural Computing (TPNC 2014) - Granada, Spain Duration: 9 Dec 2014 → 11 Dec 2014

Publication series

Name	Lecture Notes in Computer Science
Volume	8890
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on the Theory and Practice of Natural Computing (TPNC 2014)
Place	Spain
City	Granada
Period	9/12/14 → 11/12/14

Research Keywords

Linear parameter varying system
Model predictive control
Recurrent neural network

Access Output

10.1007/978-3-319-13749-0_22

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Yan, Z., Le, X., & Wang, J. (2014). Model predictive control of linear parameter varying systems based on a recurrent neural network. In A.-H. Dediu, M. Lozano, & C. Martín-Vide (Eds.), Theory and Practice of Natural Computing: Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings (pp. 255-266). (Lecture Notes in Computer Science; Vol. 8890). Springer . https://doi.org/10.1007/978-3-319-13749-0_22

Yan, Zheng ; Le, Xinyi ; Wang, Jun. / Model predictive control of linear parameter varying systems based on a recurrent neural network. Theory and Practice of Natural Computing: Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings. editor / Adrian-Horia Dediu ; Manuel Lozano ; Carlos Martín-Vide. Cham : Springer , 2014. pp. 255-266 (Lecture Notes in Computer Science).

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title = "Model predictive control of linear parameter varying systems based on a recurrent neural network",

abstract = "This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach. {\textcopyright} Springer International Publishing Switzerland 2014",

keywords = "Linear parameter varying system, Model predictive control, Recurrent neural network",

author = "Zheng Yan and Xinyi Le and Jun Wang",

year = "2014",

doi = "10.1007/978-3-319-13749-0\_22",

language = "English",

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series = "Lecture Notes in Computer Science",

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pages = "255--266",

editor = "Adrian-Horia Dediu and Manuel Lozano and Carlos Mart{\'i}n-Vide",

booktitle = "Theory and Practice of Natural Computing",

note = "3rd International Conference on the Theory and Practice of Natural Computing (TPNC 2014) ; Conference date: 09-12-2014 Through 11-12-2014",

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Yan, Z, Le, X & Wang, J 2014, Model predictive control of linear parameter varying systems based on a recurrent neural network. in A-H Dediu, M Lozano & C Martín-Vide (eds), Theory and Practice of Natural Computing: Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings. Lecture Notes in Computer Science, vol. 8890, Springer , Cham, pp. 255-266, 3rd International Conference on the Theory and Practice of Natural Computing (TPNC 2014), Granada, Spain, 9/12/14. https://doi.org/10.1007/978-3-319-13749-0_22

Model predictive control of linear parameter varying systems based on a recurrent neural network. / Yan, Zheng; Le, Xinyi; Wang, Jun.
Theory and Practice of Natural Computing: Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings. ed. / Adrian-Horia Dediu; Manuel Lozano; Carlos Martín-Vide. Cham: Springer , 2014. p. 255-266 (Lecture Notes in Computer Science; Vol. 8890).

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

TY - GEN

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AU - Yan, Zheng

AU - Le, Xinyi

AU - Wang, Jun

PY - 2014

Y1 - 2014

N2 - This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach. © Springer International Publishing Switzerland 2014

AB - This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach. © Springer International Publishing Switzerland 2014

KW - Linear parameter varying system

KW - Model predictive control

KW - Recurrent neural network

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U2 - 10.1007/978-3-319-13749-0_22

DO - 10.1007/978-3-319-13749-0_22

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

SN - 9783319137483

T3 - Lecture Notes in Computer Science

SP - 255

EP - 266

BT - Theory and Practice of Natural Computing

A2 - Dediu, Adrian-Horia

A2 - Lozano, Manuel

A2 - Martín-Vide, Carlos

PB - Springer

CY - Cham

T2 - 3rd International Conference on the Theory and Practice of Natural Computing (TPNC 2014)

Y2 - 9 December 2014 through 11 December 2014

ER -

Yan Z, Le X, Wang J. Model predictive control of linear parameter varying systems based on a recurrent neural network. In Dediu AH, Lozano M, Martín-Vide C, editors, Theory and Practice of Natural Computing: Third International Conference, TPNC 2014, Granada, Spain, December 9-11, 2014. Proceedings. Cham: Springer . 2014. p. 255-266. (Lecture Notes in Computer Science). Epub 2014 Dec 5. doi: 10.1007/978-3-319-13749-0_22

Model predictive control of linear parameter varying systems based on a recurrent neural network

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