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Zeroing Neural Network for Multiquadrotor Hose Transportation Problem

  • Peining Jia
  • , Haojin Li
  • , Qingfa Li*
  • , Sitian Qin
  • *Corresponding author for this work
  • School of Science, Harbin Institute of Technology Weihai
  • Heilongjiang Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a zeroing neural network (ZNN) to the multiquadrotor hose transportation problem (MHTP). The MHTP is transformed into a coupled equation system through matrix design, then, by introducing an error function, the ZNN model is proposed. Two different activation functions (AFs) are applied in the ZNN model to accelerate the convergence rate of the states. Under the efficient AFs, the states achieve fixed-time conevergence and predefined-time convergence, while the parametric control method of the convergence time is also theoretically given. Finally, the theoretical results are illustrated by a simulation.

Original languageEnglish
Title of host publication17th International Conference on Advanced Computational Intelligence, ICACI 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-302
Number of pages6
ISBN (Electronic)9798331509798
DOIs
StatePublished - 2025
Externally publishedYes
Event17th International Conference on Advanced Computational Intelligence, ICACI 2025 - Bath, United Kingdom
Duration: 7 Jul 202513 Jul 2025

Publication series

Name17th International Conference on Advanced Computational Intelligence, ICACI 2025

Conference

Conference17th International Conference on Advanced Computational Intelligence, ICACI 2025
Country/TerritoryUnited Kingdom
CityBath
Period7/07/2513/07/25

Keywords

  • Fixed-time convergence
  • Multiquadrotor hose transportation problem
  • Predefined-time convergence
  • Zeroing neural network

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