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• W2890142580 abstract "Distributed controllers connected through the shared wireless communication network are necessary in designing distributed flocking control schemes for Networked multi-Unmanned Aircraft Systems (UAS). Network imperfections such as time delay, which has been considered as a challenging issue, commonly exists in the wireless network. Therefore, network imperfections should be taken into account in designing control algorithms for networked multi-UAS. Besides of network imperfections, the uncertainty from the complex environment and system dynamics is another critical challenge and cannot be ignored in advanced applicable control development. In this paper, a biologically-inspired distributed intelligent control methodology is proposed to overcome the challenges, i.e., networked imperfections and uncertainty from environment and system, in networked multi-UAS flocking. Considering the limited computational ability in the practical onboard controller, the proposed method is adopted based on the emotional learning phenomenon in the mammalian limbic system. The learning capability and low computational complexity of the proposed technique makes it a propitious tool for implementing in real-time networked multi-UAS flocking considering the network imperfection and uncertainty from environment and system. Computer-based numerical results of the implementation of the proposed methodology demonstrate the effectiveness of this algorithm for distributed intelligent flocking control of networked multi-UAS." @default.
• W2890142580 created "2018-09-27" @default.
• W2890142580 creator A5014289538 @default.
• W2890142580 creator A5057534575 @default.
• W2890142580 date "2018-06-01" @default.
• W2890142580 modified "2023-09-25" @default.
• W2890142580 title "A Biologically-Inspired Distributed Intelligent Flocking Control for Networked Multi-UAS with Uncertain Network Imperfections" @default.
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• W2890142580 doi "https://doi.org/10.1109/icuas.2018.8453476" @default.
• W2890142580 hasPublicationYear "2018" @default.
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