Distributed model-independent consensus of Euler–Lagrange agents on directed networks
This paper proposes a distributed model-independent algorithm to achieve leaderless consensus on a directed network where each fully-actuated agent has self-dynamics described by Euler–Lagrange equations of motion. Specifically, we aim to achieve consensus of the generalised coordinates with zero ge...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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WILEY
2017
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/ DP160104500 http://hdl.handle.net/20.500.11937/84365 |
| _version_ | 1848764643328131072 |
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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 | This paper proposes a distributed model-independent algorithm to achieve leaderless consensus on a directed network where each fully-actuated agent has self-dynamics described by Euler–Lagrange equations of motion. Specifically, we aim to achieve consensus of the generalised coordinates with zero generalised velocity. We show that on a strongly connected graph, a model-independent algorithm can achieve the consensus objective at an exponential rate if an upper bound on the initial conditions is known a priori. By model-independent, we mean that each agent can execute the algorithm with no knowledge of the equations describing the self-dynamics of any agent. For design of the control laws which achieve consensus, a control gain scalar and a control gain matrix are required to satisfy several inequalities involving bounds on the matrices of the agent dynamic model, bounds on the Laplacian matrix describing the network topology and the set of initial conditions; design of the algorithm therefore requires some knowledge on the bounds of the agent dynamical parameters. Because only bounds are required, the proposed algorithm offers robustness to uncertainty in the parameters of the multiagent system. We systematically show that additional relative velocity information improves the performance of the controller. Numerical simulations are provided to show the effectiveness of the algorithm. Copyright © 2016 John Wiley & Sons, Ltd. |
| first_indexed | 2025-11-14T11:22:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-84365 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:22:37Z |
| publishDate | 2017 |
| publisher | WILEY |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-843652021-07-26T07:35:04Z Distributed model-independent consensus of Euler–Lagrange agents on directed networks Ye, Mengbin Anderson, B.D.O. Yu, C. Science & Technology Technology Physical Sciences Automation & Control Systems Engineering, Electrical & Electronic Mathematics, Applied Engineering Mathematics model-independent Euler-Lagrange system semi-global directed graph leaderless consensus MULTIAGENT SYSTEMS LEADER SYNCHRONIZATION TRACKING COORDINATION This paper proposes a distributed model-independent algorithm to achieve leaderless consensus on a directed network where each fully-actuated agent has self-dynamics described by Euler–Lagrange equations of motion. Specifically, we aim to achieve consensus of the generalised coordinates with zero generalised velocity. We show that on a strongly connected graph, a model-independent algorithm can achieve the consensus objective at an exponential rate if an upper bound on the initial conditions is known a priori. By model-independent, we mean that each agent can execute the algorithm with no knowledge of the equations describing the self-dynamics of any agent. For design of the control laws which achieve consensus, a control gain scalar and a control gain matrix are required to satisfy several inequalities involving bounds on the matrices of the agent dynamic model, bounds on the Laplacian matrix describing the network topology and the set of initial conditions; design of the algorithm therefore requires some knowledge on the bounds of the agent dynamical parameters. Because only bounds are required, the proposed algorithm offers robustness to uncertainty in the parameters of the multiagent system. We systematically show that additional relative velocity information improves the performance of the controller. Numerical simulations are provided to show the effectiveness of the algorithm. Copyright © 2016 John Wiley & Sons, Ltd. 2017 Journal Article http://hdl.handle.net/20.500.11937/84365 10.1002/rnc.3689 English http://purl.org/au-research/grants/arc/ DP160104500 http://purl.org/au-research/grants/arc/ DP130103610 WILEY fulltext |
| spellingShingle | Science & Technology Technology Physical Sciences Automation & Control Systems Engineering, Electrical & Electronic Mathematics, Applied Engineering Mathematics model-independent Euler-Lagrange system semi-global directed graph leaderless consensus MULTIAGENT SYSTEMS LEADER SYNCHRONIZATION TRACKING COORDINATION Ye, Mengbin Anderson, B.D.O. Yu, C. Distributed model-independent consensus of Euler–Lagrange agents on directed networks |
| title | Distributed model-independent consensus of Euler–Lagrange agents on directed networks |
| title_full | Distributed model-independent consensus of Euler–Lagrange agents on directed networks |
| title_fullStr | Distributed model-independent consensus of Euler–Lagrange agents on directed networks |
| title_full_unstemmed | Distributed model-independent consensus of Euler–Lagrange agents on directed networks |
| title_short | Distributed model-independent consensus of Euler–Lagrange agents on directed networks |
| title_sort | distributed model-independent consensus of euler–lagrange agents on directed networks |
| topic | Science & Technology Technology Physical Sciences Automation & Control Systems Engineering, Electrical & Electronic Mathematics, Applied Engineering Mathematics model-independent Euler-Lagrange system semi-global directed graph leaderless consensus MULTIAGENT SYSTEMS LEADER SYNCHRONIZATION TRACKING COORDINATION |
| url | http://purl.org/au-research/grants/arc/ DP160104500 http://purl.org/au-research/grants/arc/ DP160104500 http://hdl.handle.net/20.500.11937/84365 |