Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots
This paper considers the region-based formation control for a swarm of robots with unknown nonlinear dynamics and disturbances. An adaptive neural network is designed to approximate the unknown nonlinear dynamics, and the desired formation shape is achieved by designing appropriate potential functio...
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2018-01-01
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Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2018/8382702 |
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doaj-ddc203cf374d414cac01c5795993a3972020-11-25T01:11:09ZengHindawi-WileyComplexity1076-27871099-05262018-01-01201810.1155/2018/83827028382702Adaptive-Neural-Network-Based Shape Control for a Swarm of RobotsXuejing Lan0Zhenghao Wu1Wenbiao Xu2Guiyun Liu3School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaGuangdong Institute of Metrology, Guangzhou 510405, ChinaSchool of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, ChinaThis paper considers the region-based formation control for a swarm of robots with unknown nonlinear dynamics and disturbances. An adaptive neural network is designed to approximate the unknown nonlinear dynamics, and the desired formation shape is achieved by designing appropriate potential functions. Moreover, the collision avoidance, velocity consensus, and region tracking are all considered in the controller. The stability of the multirobot system has been demonstrated based on the Lyapunov theorem. Finally, three numerical simulations show the effectiveness of the proposed formation control scheme to deal with the narrow space, loss of robots, and formation merging problems.http://dx.doi.org/10.1155/2018/8382702 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xuejing Lan Zhenghao Wu Wenbiao Xu Guiyun Liu |
spellingShingle |
Xuejing Lan Zhenghao Wu Wenbiao Xu Guiyun Liu Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots Complexity |
author_facet |
Xuejing Lan Zhenghao Wu Wenbiao Xu Guiyun Liu |
author_sort |
Xuejing Lan |
title |
Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots |
title_short |
Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots |
title_full |
Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots |
title_fullStr |
Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots |
title_full_unstemmed |
Adaptive-Neural-Network-Based Shape Control for a Swarm of Robots |
title_sort |
adaptive-neural-network-based shape control for a swarm of robots |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1076-2787 1099-0526 |
publishDate |
2018-01-01 |
description |
This paper considers the region-based formation control for a swarm of robots with unknown nonlinear dynamics and disturbances. An adaptive neural network is designed to approximate the unknown nonlinear dynamics, and the desired formation shape is achieved by designing appropriate potential functions. Moreover, the collision avoidance, velocity consensus, and region tracking are all considered in the controller. The stability of the multirobot system has been demonstrated based on the Lyapunov theorem. Finally, three numerical simulations show the effectiveness of the proposed formation control scheme to deal with the narrow space, loss of robots, and formation merging problems. |
url |
http://dx.doi.org/10.1155/2018/8382702 |
work_keys_str_mv |
AT xuejinglan adaptiveneuralnetworkbasedshapecontrolforaswarmofrobots AT zhenghaowu adaptiveneuralnetworkbasedshapecontrolforaswarmofrobots AT wenbiaoxu adaptiveneuralnetworkbasedshapecontrolforaswarmofrobots AT guiyunliu adaptiveneuralnetworkbasedshapecontrolforaswarmofrobots |
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1725172727640227840 |