Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Distributed Control Multi-Agent Systems · Underwater Vehicles and Communication Systems · Adaptive Control of Nonlinear Systems
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Affiliations
Department of Computer and Information Science, University of Macau, Taipa 999078, Macau, China
School of Computer Science, North China University of Technology, Beijing 100144, China
Funding
Universidade de Macau · Fundo para o Desenvolvimento das Ciências e da Tecnologia
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