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Part 1: Document Description
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Citation |
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Title: |
Adaptive hierarchical formation control for uncertain Euler–Lagrange systems using distributed inverse dynamics |
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Identification Number: |
doi:10.34820/FK2/LSU6OD |
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Distributor: |
Telkom University Dataverse |
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Date of Distribution: |
2023-10-05 |
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Version: |
1 |
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Bibliographic Citation: |
ROSA, MUHAMMAD RIDHO, 2023, "Adaptive hierarchical formation control for uncertain Euler–Lagrange systems using distributed inverse dynamics", https://doi.org/10.34820/FK2/LSU6OD, Telkom University Dataverse, V1 |
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Citation |
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Title: |
Adaptive hierarchical formation control for uncertain Euler–Lagrange systems using distributed inverse dynamics |
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Identification Number: |
doi:10.34820/FK2/LSU6OD |
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Authoring Entity: |
ROSA, MUHAMMAD RIDHO (Fakultas Teknik Elektro) |
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Distributor: |
Telkom University Dataverse |
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Access Authority: |
ROSA, MUHAMMAD RIDHO |
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Depositor: |
ROSA, MUHAMMAD RIDHO |
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Date of Deposit: |
2022-04-13 |
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Study Scope |
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Keywords: |
Engineering |
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Abstract: |
This paper establishes a novel adaptive hierarchical formation control method for uncertain heterogeneous nonlinear agents described by Euler–Lagrange (EL) dynamics. Formation control is framed as a synchronization problem where a distributed model reference adaptive control is used to synchronize the EL systems. The idea behind the proposed adaptive formation algorithm is that each agent must converge to the model defined by its hierarchically superior neighbors. Using a distributed inverse dynamics structure, we prove that distributed nonlinear matching conditions between connected agents hold, so that matching gains exist to make the entire formation converge to same homogeneous dynamics: to compensate for the presence of uncertainties, estimation laws are devised for such matching gains, leading to adaptive synchronization. An appropriately designed distributed Lyapunov function is used to derive asymptotic convergence of the synchronization error. The effectiveness of the proposed methodology is supported by simulations of a formation of Unmanned Aerial Vehicles (UAVs). |
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Notes: |
This work has been partially supported by MULTI-COORD: “Multi-agent Coordination with Networked-induced Constraints”, Joint Project TU Delft – China State Shipbuilding Corporation, by NEUROCON: “Adaptive Neural Control for Uncertain Nonlinear Systems”, Joint Project TU Delft – Xian Jiaotong, and by the Indonesia Endowment Fund for Education (LPDP), Grant No. S-6411/LPDP.3/2015. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
CC0 Waiver |
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Other Study Description Materials |
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