Model-independent trajectory tracking of Euler-Lagrange agents on directed networks

The problem of trajectory tracking of a moving leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics is studied in this paper using a distributed, model-independent control law. We show that if the directed graph contains a directed spanning tree, with the le...

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Main Authors: Ye, Mengbin, Anderson, B.D.O., Yu, C.
Format: Conference Paper
Language:English
Published: IEEE 2016
Subjects:
Online Access:http://purl.org/au-research/grants/arc/DP160104500
http://hdl.handle.net/20.500.11937/84364
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author Ye, Mengbin
Anderson, B.D.O.
Yu, C.
author_facet Ye, Mengbin
Anderson, B.D.O.
Yu, C.
author_sort Ye, Mengbin
building Curtin Institutional Repository
collection Online Access
description The problem of trajectory tracking of a moving leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics is studied in this paper using a distributed, model-independent control law. We show that if the directed graph contains a directed spanning tree, with the leader as the root node, then a model-independent algorithm semi-globally achieves the trajectory tracking objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the agent self-dynamics, though reasonably, certain bounds are known. For stability, a pair of control gains for each agent are required to satisfy lower bounding inequalities and so design of the algorithm is centralised and requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness.
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publishDate 2016
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spelling curtin-20.500.11937-843642022-10-27T04:58:54Z Model-independent trajectory tracking of Euler-Lagrange agents on directed networks Ye, Mengbin Anderson, B.D.O. Yu, C. Science & Technology Technology Automation & Control Systems Engineering, Electrical & Electronic Operations Research & Management Science Engineering SYSTEMS SYNCHRONIZATION COORDINATION The problem of trajectory tracking of a moving leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics is studied in this paper using a distributed, model-independent control law. We show that if the directed graph contains a directed spanning tree, with the leader as the root node, then a model-independent algorithm semi-globally achieves the trajectory tracking objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the agent self-dynamics, though reasonably, certain bounds are known. For stability, a pair of control gains for each agent are required to satisfy lower bounding inequalities and so design of the algorithm is centralised and requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness. 2016 Conference Paper http://hdl.handle.net/20.500.11937/84364 10.1109/CDC.2016.7799335 English http://purl.org/au-research/grants/arc/DP160104500 IEEE fulltext
spellingShingle Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Operations Research & Management Science
Engineering
SYSTEMS
SYNCHRONIZATION
COORDINATION
Ye, Mengbin
Anderson, B.D.O.
Yu, C.
Model-independent trajectory tracking of Euler-Lagrange agents on directed networks
title Model-independent trajectory tracking of Euler-Lagrange agents on directed networks
title_full Model-independent trajectory tracking of Euler-Lagrange agents on directed networks
title_fullStr Model-independent trajectory tracking of Euler-Lagrange agents on directed networks
title_full_unstemmed Model-independent trajectory tracking of Euler-Lagrange agents on directed networks
title_short Model-independent trajectory tracking of Euler-Lagrange agents on directed networks
title_sort model-independent trajectory tracking of euler-lagrange agents on directed networks
topic Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Operations Research & Management Science
Engineering
SYSTEMS
SYNCHRONIZATION
COORDINATION
url http://purl.org/au-research/grants/arc/DP160104500
http://hdl.handle.net/20.500.11937/84364