Distributed Event-Triggered Adaptive Formation Tracking of Networked Uncertain Stratospheric Airships Using Neural Networksopen access
- Authors
- Kim, Jin Hoe; Yoo, Sung Jin
- Issue Date
- Mar-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Adaptive systems; Atmospheric modeling; Neural networks; Attitude control; Stability analysis; Adaptation models; Aerospace electronics; Distributed adaptive formation tracking; event-triggered; neural networks; networked stratospheric airships
- Citation
- IEEE ACCESS, v.8, pp 49977 - 49988
- Pages
- 12
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 49977
- End Page
- 49988
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40067
- DOI
- 10.1109/ACCESS.2020.2979995
- ISSN
- 2169-3536
- Abstract
- This paper investigates a distributed event-triggered formation tracking problem of networked three-dimensional uncertain nonlinear stratospheric airships under directed networks. It is assumed that the nonlinearities of airship followers are unknown and the leader information can be obtained by only a subset of the airship followers. Approximation-based local adaptive tracking controllers with asynchronous event-triggering laws are developed to achieve the desired formations for both the positions and attitudes of uncertain stratospheric airship followers. We theoretically show that the stability and formation tracking performance of event-triggered closed-loop systems are ensured and Zeno behavior is excluded in the proposed asynchronous event-triggering mechanism. Finally, simulations illustrate the effectiveness of the proposed formation control protocol.
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