Model predictive control for tracking of underactuated vessels based on recurrent neural networks

Yan, Z; Wang, J

Model predictive control for tracking of underactuated vessels based on recurrent neural networks

Yan, Z

Wang, J

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Publication Type:: Journal Article
Citation:: IEEE Journal of Oceanic Engineering, 2012, 37 (4), pp. 717 - 726
Issue Date:: 2012-07-25

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Field	Value	Language
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2012-07-25	en_US
dc.identifier.citation	IEEE Journal of Oceanic Engineering, 2012, 37 (4), pp. 717 - 726	en_US
dc.identifier.issn	0364-9059	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119083
dc.description.abstract	In this paper, a model predictive control (MPC) scheme is presented for tracking of underactuated vessels with only two available controls: namely, surge force and yaw moment. When no external disturbance is explicitly considered, the proposed MPC approach iteratively solves a formulated quadratic programming (QP) problem using a single-layer recurrent neural network called the general projection network over a finite receding horizon. When additive disturbances are taken into account, a reformulated minimax optimization problem is iteratively solved by using a two-layer recurrent neural network. The applied neural networks are both stable in the sense of Lyapunov and globally convergent to the exact optimal solutions of reformulated convex programming problems. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed neurodynamics-based MPC approaches to vessel tracking control. © 1976-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Journal of Oceanic Engineering	en_US
dc.relation.isbasedon	10.1109/JOE.2012.2201797	en_US
dc.subject.classification	Oceanography	en_US
dc.title	Model predictive control for tracking of underactuated vessels based on recurrent neural networks	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	37	en_US
utslib.for	0911 Maritime Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	37	en_US

Abstract:

In this paper, a model predictive control (MPC) scheme is presented for tracking of underactuated vessels with only two available controls: namely, surge force and yaw moment. When no external disturbance is explicitly considered, the proposed MPC approach iteratively solves a formulated quadratic programming (QP) problem using a single-layer recurrent neural network called the general projection network over a finite receding horizon. When additive disturbances are taken into account, a reformulated minimax optimization problem is iteratively solved by using a two-layer recurrent neural network. The applied neural networks are both stable in the sense of Lyapunov and globally convergent to the exact optimal solutions of reformulated convex programming problems. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed neurodynamics-based MPC approaches to vessel tracking control. © 1976-2012 IEEE.

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http://hdl.handle.net/10453/119083