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Hydrodynamic Modeling and Parameter Identification of a Bionic Underwater Vehicle: RobDact.
Cao, Qiyuan; Wang, Rui; Zhang, Tiandong; Wang, Yu; Wang, Shuo.
Afiliación
  • Cao Q; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Wang R; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • Zhang T; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Wang Y; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Wang S; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Cyborg Bionic Syst ; 2022: 9806328, 2022.
Article en En | MEDLINE | ID: mdl-36285303
In this paper, the hydrodynamic modeling and parameter identification of the RobDact, a bionic underwater vehicle inspired by Dactylopteridae, are carried out based on computational fluid dynamics (CFD) and force measurement experiment. Firstly, the paper briefly describes the RobDact, then establishes the kinematics model and rigid body dynamics model of the RobDact according to the hydrodynamic force and moment equations. Through CFD simulations, the hydrodynamic force of the RobDact at different speeds is obtained, and then, the hydrodynamic model parameters are identified. Furthermore, the measurement platform is developed to obtain the relationship between the thrust generated by the RobDact and the input fluctuation parameters. Finally, by combining the rigid body dynamics model and the fin thrust mapping model, the hydrodynamic model of the RobDact at different motion states is constructed.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Cyborg Bionic Syst Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Cyborg Bionic Syst Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos