Extended-state-observer-based distributed model predictive formation control of under-actuated unmanned surface vehicles with collision avoidance
In this paper, distributed formation tracking control with collision avoidance is addressed for a group of under-actuated unmanned surface vehicles subject to physical constraints and dynamical uncertainties. An extended-state-observer-based distributed model predictive control method is proposed for achieving a safe formation. Specifically, the vehicle dynamics is firstly transformed into an almost spherical form consisting of a position motion subsystem and an angular motion subsystem. Next, an extended state observer is used to estimate unknown model uncertainties and external disturbances in each subsystem. After that, by taking physical constraints and collision avoidance requirements into account, a distributed model predictive position tracking controller and a model predictive angular motion controller are designed based on the recovered model information through the extended state observers. The distributed formation control with collision avoidance problem is formulated as a constrained quadratic programming problem, which can be locally solved in a decentralized manner. Finally, the simulation results of five under-actuated unmanned surface vehicles substantiate the effectiveness of the proposed extended-state-observer-based distributed model predictive control method for multiple under-actuated unmanned surface vehicles.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
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Supplemental Notes:
- © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Lv, Guanghao
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0000-0001-7911-5174
- Peng, Zhouhua
- Wang, Haoliang
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0000-0003-4222-9893
- Liu, Lu
- Wang, Dan
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0000-0002-4099-6004
- Li, Tieshan
- Publication Date: 2021-10-15
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
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Serial:
- Ocean Engineering
- Volume: 238
- Issue Number: 0
- Publisher: Pergamon
- ISSN: 0029-8018
- EISSN: 1873-5258
- Serial URL: http://www.sciencedirect.com/science/journal/00298018
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Crash avoidance systems; Marine safety; Predictive models; Ships
- Subject Areas: Marine Transportation; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01781693
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 20 2021 2:52PM