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

Zhiying Liu, Xinzhe Wang, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3167-3172
Number of pages6
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 3 Sep 2014
Externally publishedYes
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

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