Model predictive control of underwater gliders based on a one-layer recurrent neural network

Yuan Shan; Zheng Yan; Jun Wang

doi:10.1109/ICACI.2013.6748525

Model predictive control of underwater gliders based on a one-layer recurrent neural network

Yuan Shan
, Zheng Yan
, Jun Wang

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

23 Citations (Scopus)

Abstract

In this paper, a motion control problem for underwater gilders in longitudinal plane is considered. A recurrent neural network based model predictive control approach is developed. The model predictive control of underwater gliders is formulated as a time-varying constrained quadratic programming problem, which is solved by using a recurrent neural network called the simplified dual network in real-time. Simulation results are further presented to show the effectiveness and performance of the proposed model predictive control approach. © 2013 IEEE.

Original language	English
Title of host publication	2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings
Publisher	IEEE Computer Society
Pages	328-333
ISBN (Print)	9781467363433
DOIs	https://doi.org/10.1109/ICACI.2013.6748525
Publication status	Published - 2013
Externally published	Yes
Event	2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Hangzhou, Zhejiang, China Duration: 19 Oct 2013 → 21 Oct 2013

Conference

Conference	2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013
Place	China
City	Hangzhou, Zhejiang
Period	19/10/13 → 21/10/13

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10.1109/ICACI.2013.6748525

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title = "Model predictive control of underwater gliders based on a one-layer recurrent neural network",

abstract = "In this paper, a motion control problem for underwater gilders in longitudinal plane is considered. A recurrent neural network based model predictive control approach is developed. The model predictive control of underwater gliders is formulated as a time-varying constrained quadratic programming problem, which is solved by using a recurrent neural network called the simplified dual network in real-time. Simulation results are further presented to show the effectiveness and performance of the proposed model predictive control approach. {\textcopyright} 2013 IEEE.",

author = "Yuan Shan and Zheng Yan and Jun Wang",

year = "2013",

doi = "10.1109/ICACI.2013.6748525",

language = "English",

isbn = "9781467363433",

pages = "328--333",

booktitle = "2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings",

publisher = "IEEE Computer Society",

address = "United States",

note = "2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 ; Conference date: 19-10-2013 Through 21-10-2013",

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Shan, Y, Yan, Z & Wang, J 2013, Model predictive control of underwater gliders based on a one-layer recurrent neural network. in 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings., 6748525, IEEE Computer Society, pp. 328-333, 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013, Hangzhou, Zhejiang, China, 19/10/13. https://doi.org/10.1109/ICACI.2013.6748525

Model predictive control of underwater gliders based on a one-layer recurrent neural network. / Shan, Yuan; Yan, Zheng; Wang, Jun.
2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings. IEEE Computer Society, 2013. p. 328-333 6748525.

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

TY - GEN

T1 - Model predictive control of underwater gliders based on a one-layer recurrent neural network

AU - Shan, Yuan

AU - Yan, Zheng

AU - Wang, Jun

PY - 2013

Y1 - 2013

N2 - In this paper, a motion control problem for underwater gilders in longitudinal plane is considered. A recurrent neural network based model predictive control approach is developed. The model predictive control of underwater gliders is formulated as a time-varying constrained quadratic programming problem, which is solved by using a recurrent neural network called the simplified dual network in real-time. Simulation results are further presented to show the effectiveness and performance of the proposed model predictive control approach. © 2013 IEEE.

AB - In this paper, a motion control problem for underwater gilders in longitudinal plane is considered. A recurrent neural network based model predictive control approach is developed. The model predictive control of underwater gliders is formulated as a time-varying constrained quadratic programming problem, which is solved by using a recurrent neural network called the simplified dual network in real-time. Simulation results are further presented to show the effectiveness and performance of the proposed model predictive control approach. © 2013 IEEE.

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U2 - 10.1109/ICACI.2013.6748525

DO - 10.1109/ICACI.2013.6748525

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

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SP - 328

EP - 333

BT - 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013 - Proceedings

PB - IEEE Computer Society

T2 - 2013 6th International Conference on Advanced Computational Intelligence, ICACI 2013

Y2 - 19 October 2013 through 21 October 2013

ER -

Model predictive control of underwater gliders based on a one-layer recurrent neural network

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