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Open AccessArticle

Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms

1
Department of Computer and Information Science, University of Macau, Taipa 999078, Macau, China
2
School of Computer Science, North China University of Technology, Beijing 100144, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2018, 8(6), 862; https://doi.org/10.3390/app8060862
Received: 17 April 2018 / Revised: 16 May 2018 / Accepted: 22 May 2018 / Published: 24 May 2018
(This article belongs to the Special Issue Multi-Agent Systems)
In this paper, the problem of coordinated control of multiple hovercrafts is addressed. For a single hovercraft, by using the backstepping technique, a nonlinear controller is proposed, where Radial Basis Function Neural Networks (RBFNNs) are adopted to approximate unmodeled terms. Despite the application of RBFNNs, integral terms are introduced, improving the robustness of controller. As a result, global uniformly ultimate boundedness is achieved. Regarding the communication topology, two different directed graphs are chosen under the assumption that there are no delays when they communicate with each other. In order to testify the performance of the proposed strategy, simulation results are presented, showing that vehicles can move forward in a specific formation pattern and RBFNNs are able to approximate unmodeled terms. View Full-Text
Keywords: surface vehicle; underactuated vehicle; RBFNNs; directed graph; coordinated control surface vehicle; underactuated vehicle; RBFNNs; directed graph; coordinated control
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Duan, K.; Fong, S.; Zhuang, Y.; Song, W. Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Appl. Sci. 2018, 8, 862.

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