Neurodynamics-based model predictive control of autonomous underwater vehicles in vertical plane

Zhiying Liu; Xinzhe Wang; Jun Wang

doi:10.1109/IJCNN.2014.6889492

Neurodynamics-based model predictive control of autonomous underwater vehicles in vertical plane

Zhiying Liu
, Xinzhe Wang
, Jun Wang

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

4 Citations (Scopus)

Abstract

This paper presents a model predictive control (MPC) method based on a recurrent neural network for control of autonomous underwater vehicles (AUVs) in a vertical plane. Both kinematic and dynamic models are considered in the set-point control of the AUV. A one-layer recurrent neural network called the general projection neural network is applied for real-time optimization to compute optimal control vaiables. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	IEEE
Pages	3167-3172
ISBN (Print)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889492
Publication status	Published - 3 Sept 2014
Externally published	Yes
Event	2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Conference

Conference	2014 International Joint Conference on Neural Networks, IJCNN 2014
Place	China
City	Beijing
Period	6/07/14 → 11/07/14

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10.1109/IJCNN.2014.6889492

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@inproceedings{afef315dae3e40c3b9c133b9ccf37b89,

title = "Neurodynamics-based model predictive control of autonomous underwater vehicles in vertical plane",

abstract = "This paper presents a model predictive control (MPC) method based on a recurrent neural network for control of autonomous underwater vehicles (AUVs) in a vertical plane. Both kinematic and dynamic models are considered in the set-point control of the AUV. A one-layer recurrent neural network called the general projection neural network is applied for real-time optimization to compute optimal control vaiables. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method.",

author = "Zhiying Liu and Xinzhe Wang and Jun Wang",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/IJCNN.2014.6889492",

language = "English",

isbn = "9781479914845",

pages = "3167--3172",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "IEEE",

address = "United States",

note = "2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

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Neurodynamics-based model predictive control of autonomous underwater vehicles in vertical plane. / Liu, Zhiying; Wang, Xinzhe; Wang, Jun.
Proceedings of the International Joint Conference on Neural Networks. IEEE, 2014. p. 3167-3172 6889492.

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

TY - GEN

T1 - Neurodynamics-based model predictive control of autonomous underwater vehicles in vertical plane

AU - Liu, Zhiying

AU - Wang, Xinzhe

AU - Wang, Jun

PY - 2014/9/3

Y1 - 2014/9/3

N2 - This paper presents a model predictive control (MPC) method based on a recurrent neural network for control of autonomous underwater vehicles (AUVs) in a vertical plane. Both kinematic and dynamic models are considered in the set-point control of the AUV. A one-layer recurrent neural network called the general projection neural network is applied for real-time optimization to compute optimal control vaiables. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method.

AB - This paper presents a model predictive control (MPC) method based on a recurrent neural network for control of autonomous underwater vehicles (AUVs) in a vertical plane. Both kinematic and dynamic models are considered in the set-point control of the AUV. A one-layer recurrent neural network called the general projection neural network is applied for real-time optimization to compute optimal control vaiables. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method.

UR - https://www.scopus.com/pages/publications/84908474579

UR - https://www.scopus.com/pages/publications/84908474579#tab=impact

U2 - 10.1109/IJCNN.2014.6889492

DO - 10.1109/IJCNN.2014.6889492

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

SN - 9781479914845

SP - 3167

EP - 3172

BT - Proceedings of the International Joint Conference on Neural Networks

PB - IEEE

T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

Neurodynamics-based model predictive control of autonomous underwater vehicles in vertical plane

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