Model predictive control of multi-robot formation based on the simplified dual neural network

Wang, X; Yan, Z; Wang, J

Model predictive control of multi-robot formation based on the simplified dual neural network

Wang, X Yan, Z

Wang, J

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 3161 - 3166
Issue Date:: 2014-01-01

Closed Access

	Filename	Description	Size
	m.pdf	Published version	749.81 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Wang, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 3161 - 3166	en_US
dc.identifier.isbn	9781479914845	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121305
dc.description.abstract	© 2014 IEEE. This paper is concerned with formation control problems of multi-robot systems in framework of model predictive control. The formation control of robots herein is based on the leader-follower scheme. The followers are controlled by torques to track the desired trajectories to form and keep a formation. A model predictive control approach is proposed for solving the formation control problem, where the control problem is formulated as a dynamic quadratic optimization problem. A one-layer recurrent neural network called the simplified dual network is applied for computing the optimal control input in real time. Simulation results substantiate that the formation of robots can be well controlled by the proposed approach.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2014.6889491	en_US
dc.title	Model predictive control of multi-robot formation based on the simplified dual neural network	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US

Abstract:

© 2014 IEEE. This paper is concerned with formation control problems of multi-robot systems in framework of model predictive control. The formation control of robots herein is based on the leader-follower scheme. The followers are controlled by torques to track the desired trajectories to form and keep a formation. A model predictive control approach is proposed for solving the formation control problem, where the control problem is formulated as a dynamic quadratic optimization problem. A one-layer recurrent neural network called the simplified dual network is applied for computing the optimal control input in real time. Simulation results substantiate that the formation of robots can be well controlled by the proposed approach.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121305